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Brazil fan world cup 2002 nipple slip big boobs bouncing. How to casually ask a girl out. Vintage hardy perfect fly reel. Pictures of me nude. Journal of modern history gay marriage. Natalie grey nude. Free milf photo wife. When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results. While there Fully public facial many databases in use currently, the choice of an appropriate database to be used should be made based on the task given aging, expressions, lighting etc. Another way is to choose the data set specific to the property to be tested e. Li click Anil K. Jain, ed. To the best of our knowledge this is the first available benchmark that directly assesses the accuracy of algorithms to automatically verify the compliance of face images to the ISO standard, in the attempt Fully public facial semi-automating the document issuing process. Jonathon Phillips, A. Martin, C. Wilson, M. Mansfield, J. Delac, M. Grgic, S. The FERET program set out to establish a large database of facial images that was gathered independently from the algorithm developers. Harry Wechsler Fully public facial George Mason University was selected to direct the collection of this database. The database Fully public facial was a collaborative effort between Dr. Shahrukh fucking depika nude Definicion de anales de la historia.

Bootys ghetto naked. And that brings the discussion to the concerns Fully public facial by the United States and the United Kingdom, in particular, about the security risks associated with Huawei, including the allegations that the company might facilitate intelligence collection for Beijing.

Huawei won't get it all, in any case. After that, the process will accelerate. And for Lysenko, this is all about industry and automation. We are also supporting big companies like Yandex exchanging with them the data needed for the development of new services.

It took us just two years to go from the first basic tests to a full-fledged public robotaxi service. Now, thanks to our agreement with Hyundai Mobis, we will be able to move even faster. After lab testing, it is possible to perform outdoor testing. So far the most successful is Yandex, whose autonomous car has already been tested on Moscow streets. About 30 other companies have the potential to start testing in the Fully public facial future, including KAMAZ, whose autonomous bus and autonomous truck are now at the Fully public facial of lab Fully public facial.

And it isn't just cars. The tram is now being tested in the depot. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport.

Nutcracker Xxxx Watch Video Porno maso. The database is freely available to the scientific community. Statistics and details about the annotations can be found on the " About " page. Fifty people used VR glasses to watch a broadcast. Stadium cameras broadcast to a 5G cell tower, and the cell tower transmitted to smartphones connected to the VR glasses, using up to 35 Mbps per device. And that brings the discussion to the concerns raised by the United States and the United Kingdom, in particular, about the security risks associated with Huawei, including the allegations that the company might facilitate intelligence collection for Beijing. Huawei won't get it all, in any case. After that, the process will accelerate. And for Lysenko, this is all about industry and automation. We are also supporting big companies like Yandex exchanging with them the data needed for the development of new services. It took us just two years to go from the first basic tests to a full-fledged public robotaxi service. Now, thanks to our agreement with Hyundai Mobis, we will be able to move even faster. After lab testing, it is possible to perform outdoor testing. So far the most successful is Yandex, whose autonomous car has already been tested on Moscow streets. About 30 other companies have the potential to start testing in the nearest future, including KAMAZ, whose autonomous bus and autonomous truck are now at the stage of lab testing. At that rate, assuming that false rejections occur at a regular interval and are not back-loaded, which would delay boarding even further , CBP would need more than 50 minutes from the beginning of boarding just to screen rejected travelers. Separately, approximately 40, passengers depart on international flights from JFK each day. At Boston Logan International Airport, , international travelers deplaned or boarded during the month of January If half of those passengers were outbound departures, , passengers in January—or 7, passengers each day—departed from Boston Logan International Airport. At a rate of 1 in 25, that would mean passengers would be wrongfully denied boarding at Logan Airport on a daily basis. See infra Section C. See infra Section D. DHS should justify its investment in face scans by supplying evidence of the problem it purportedly solves. DHS should stop scanning the faces of American citizens as they leave the country. DHS should prove that airport face scans are capable of identifying impostors without inconveniencing everyone else. DHS should adopt a public policy that prohibits secondary uses of the data collected by its airport face scan program. DHS should provide fairness and privacy guarantees to the airlines with which it partners. Figure 2: A traveler has his face scanned as a Customs and Border Protection agent provides instruction. Associated Press, all rights reserved. Sidebar 1: What Is Biometric Exit? Partner Process 4 June 12, , https: Because accuracy is highly dependent on image quality, the inclusion of photos from sources other than passport and visa databases, such as law enforcement encounters, likely lowers overall system accuracy rates beyond what is assumed in this paper. Partner Process, supra note 4, at 3—4. See id. Regulatory Impact Analysis 67—68 Apr. Homeland Security officials say they believe the entry and exit biometric system can also be used to crack down on illegal immigration. In the absence of a biometric entry and exit system, the agency depends on incomplete data from airline passenger manifests to track people who leave the country. Sidebar 2: National Commission on Terrorist Attacks upon the U. This includes foreign nationals except those who are under the age of 14, over the age of 79, and diplomats. See Ron Nixon, supra note See supra note 9, at 8. We think it gives us immigration and counterterrorism benefits. See supra note When courts review the text of a law to determine congressional intent, courts will often apply a canon of statutory construction known as expressio unius est exclusio alterius , or more plainly, the expression-exclusion rule. Vonn, U. See also Chevron U. Echazabal, U. Crawford, Construction of Statutes ; Ford v. Under this canon of statutory construction, courts would likely read the aforementioned nine laws and conclude that Congress did not authorize face scans of Americans exiting the country. Examining the Problem of Visa Overstays: A Need for Better Tracking and Accountability: Customs and Border Protection; and Louis A. Immigration and Customs Enforcement as of June , https: Department of Homeland Security, F. See, e. Customs and Border Protection Aug. Customs and Border Protection, July 11, , https: See 73 Fed. For some participating airlines, for instance, a traveler may request not to participate in the TVS and instead present credentials to airline personnel. Partner Process, supra note 4, at However, in a more recent public meeting, in response to a question about testing for accuracy, a DHS spokesperson acknowledged it cannot measure impostor rates, stating: DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. At a False Reject rate of 1 in 1, travelers, the 38 most recent algorithms studied produced an average False Accept rate of 9. Lowering the rate of false rejects to 1 in , travelers raised the average rate of False Accepts to more than 27 percent. The recordings are done under controlled conditions, with frontal-view and neutral expression. In the third session, 3D mask attacks are captured by a single operator attacker. If you use this database please cite this publication: Erdogmus and S. Source code to reproduce experiments in the paper: Senthilkumar Face Database Version 1. The Senthilkumar Face Database contains 80 grayscale face images of 5 people all are men , including frontal views of faces with different facial expressions, occlusions and brightness conditions. Each person has 16 different images. The face portion of the image is manually cropped to x pixels and then it is normalized. Facial images are available in both grayscale and colour images. This database contains video frames of x resolution from 60 video sequences, each of which recorded from a different subject 31 female and 29 male. Each video was collected in a different environment indoor or outdoor resulting arbitrary illumination conditions and background clutter. Furthermore, the subjects were completely free in their movements, leading to arbitrary face scales, arbitrary facial expressions, head pose in yaw, pitch and roll , motion blur, and local or global occlusions. SiblingsDB Database. The SiblingsDB contains two different datasets depicting images of individuals related by sibling relationships. The first, called HQfaces, contains a set of high quality images depicting individuals 92 pairs of siblings. A subset of 79 pairs contains profile images as well, and 56 of them have also smiling frontal and profile pictures. All the images are annotated with, respectively, the position of 76 landmarks on frontal images and 12 landmarks on profile images. The second DB, called LQfaces, contains contains 98 pairs of siblings individuals found over the Internet, where most of the subjects are celebrities. The Adience image set and benchmark of unfiltered faces for age, gender and subject classification. The dataset consists of 26, images, portraying 2, individuals, classified for 8 age groups, gender and including subject labels identity. It is unique in its construction: The sources of the images included in this set are Flickr albums, assembled by automatic upload from iPhone5 or later smartphone devices, and released by their authors to the general public under the Creative Commons CC license. This constitutes the largest, fully unconstrained collection of images for age, gender and subject recognition. Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we developed an approach to building face datasets that detects faces in images returned from searches for public figures on the Internet, followed by automatically discarding those not belonging to each queried person. The FaceScrub dataset was created using this approach, followed by manually checking and cleaning the results. It comprises a total of , face images of celebrities, with about images per person. As such, it is one of the largest public face databases. Frontalization is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses. Authors provide frontalized versions of both the widely used Labeled Faces in the Wild set LFW for face identity verification and the Adience collection for age and gender classification. These sets, LFW3D and Adience3D are made available along with our implementation of the method used for the frontalization. Indian Movie Face database IMFDB is a large unconstrained face database consisting of images of Indian actors collected from more than videos. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications. Currently, there are over 0. In addition to these faces, useful meta data are released: So, mining experiments can also be performed. This is an unique property of this benchmark compared to others. It is a database of 10, natural face photographs of all different individuals, and major celebrities removed. This database was made by randomly sampling Google Images for randomly generated names based on name distributions in the US Census. Because of this methodology, the distribution of the faces matches the demographic distribution of the US e. The database also has a wide range of faces in terms of attractiveness and emotion. Ovals surround each face to eliminate any background effects. This database contains stereo videos of 27 adult subjects 12 females and 15 males with different ethnicities. The database also includes 66 facial landmark points of each image in the database. A newly created high-resolution 3D dynamic facial expression database are presented, which is made available to the scientific research community. The 3D facial expressions are captured at a video rate 25 frames per second. For each subject, there are six model sequences showing six prototypic facial expressions anger, disgust, happiness, fear, sadness, and surprise , respectively. Each expression sequence contains about frames. The database contains 3D facial expression sequences captured from subjects, with a total of approximately 60, frame models. Each 3D model of a 3D video sequence has the resolution of approximately 35, vertices. BP4D-Spontanous Database. Therefore, newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults is introduced - BP4D-Spontanous: Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action. The database includes 41 participants 23 women, 18 men. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions. The database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. The database is in the size of about 2. The database contains 3D face and hand scans. It was acquired using the structured light technology. According to our knowledge it is the first publicly available database where both sides of a hand were captured within one scan. Although there is a large amount of research examining the perception of emotional facial expressions, almost all of this research has focused on the perception of adult facial expressions. There are several excellent stimulus sets of adult facial expressions that can be easily obtained and used in scientific research i. However, there is no complete stimulus set of child affective facial expressions, and thus research on the perception of children making affective facial expression is sparse. In order to fully understand how humans respond to and process affective facial expressions, it is important to have this understanding across a variety of means. The Child Affective Facial Expressions Set CAFE is the first attempt to create a large and representative set of children making a variety of affective facial expressions that can be used for scientific research in this area. The set is made up of photographs of over child models ages making 7 different facial expressions - happy, angry, sad, fearful, surprise, neutral, and disgust. It is mainly intended to be used for benchmarking of the face identification methods, however it is possible to use this corpus in many related tasks e. Two different partitions of the database are available. The first one contains the cropped faces that were automatically extracted from the photographs using the Viola-Jones algorithm. The face size is thus almost uniform and the images contain just a small portion of background. The images in the second partition have more background, the face size also significantly differs and the faces are not localized. The purpose of this set is to evaluate and compare complete face recognition systems where the face detection and extraction is included. Each photograph is annotated with the name of a person. There are facial images for 13 IRTT students. They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of The actual size of cropped faces x and they are further resized to downscale factor 5. Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also. The database version 1. There are facial images for 10 IRTT girl students all are female with 10 faces per subject with age factor around 23 to 24 years. The colour images along with background are captured with a pixel resolution of x and their faces are cropped to x pixels. This IRTT student video database contains one video in. Later more videos will be included in this database. The video duration is This video is captured by smart phone. The faces and other features like eyes, lips and nose are extracted from this video separately. Part one is a set of color photographs that include a total of faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. The database is designed for providing high-quality HD multi-subject banchmarked video inputs for face recognition algorithms. The database is a useful input for offline as well as online Real-Time Video scenarios. It is harvested from Google image search. The dataset contains annotated cartoon faces of famous personalities of the world with varying profession. Additionally, we also provide real faces of the public figure to study cross modal retrieval tasks, such as, Photo2Cartoon retrieval. The IIIT-CFW can be used for the study spectrum of problems, such as, face synthesis, heterogeneous face recognition, cross modal retrieval, etc. Please use this database only for the academic research purpose. The database contains multiple face images of six stylized characters. The database contains facial expression images of six stylized characters. The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise. The dataset contains 3, images of 1, celebrities. Specs on Faces SoF Dataset. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions. The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions nose and mouth added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty easy, medium, and hard. That enlarges the number of images to be 42, images 26, male images and 16, female images..

As DHS invests hundreds of Fully public facial of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader yoshizawa bakobako gangbang and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports.

But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. Some airlines may well begin to explore ways to further monetize the technology they develop, for example by enhancing targeted advertising capabilities in airports. Some, if not all, of what airlines and Fully public facial partners may do with any data or technology to which they gain access through participation in biometric exit may be constrained by agreements with DHS.

Without greater transparency regarding such private agreements and without substantive rules governing the role of private entities Fully public facial the biometric exit process, there are few protections to ensure that biometric exit data and technology will not Fully public facial abused.

As it currently stands, the biometric exit program is unjustified. If the program is indeed designed to address visa overstay travel fraud, then DHS should Fully public facial how often Fully public facial type of fraud likely occurs, publish the results, and demonstrate that it is a problem worth solving. This could be done using data already available to the agency. For example, DHS could review historical data concerning the incidence of visa overstays entering U.

DHS should suspend all airport face scans at departure gates until it comes into compliance with federal administrative law. As detailed above, the law requires DHS to solicit and consider comments from the public before adopting big-impact new programs like mandatory biometric scans.

Bhen chot Watch Video Katrina sexy.com. Recently recordings of naturalistic expressions have been added too. The database consists of over videos and high-resolution still images of 75 subjects. At the moment the Moscow Metro is analyzing international experience and searching for solutions suitable for Moscow. Beyond transportation, the area Lysenko wants to focus on is healthcare, an area where 5G and the IoT will bring wholesale change. Huawei CPE was used for the performance of remote ultrasound diagnostics and genetic sequencing. The test showed that response time was enough for comfortable remote work of the health specialists. Given its experience at the World Cup, Moscow has some real-world 5G data to help guide deployments and manage expectations. Theoretically, the 5G record in standard circumstances can be 35 Gbps, meaning less than three seconds to download an HD-movie. That said, testing in the U. Moscow still has the World Cup 5G equipment which will be reused in the pilots. To achieve this goal, we freely exchange ideas and experience in smart city development, including AI applications, with representatives of other cities worldwide. We broadly use public platforms like GitHub — to share our algorithms with developers around the world. The response of those citizens to , facial recognition cameras though, citywide, remains to be seen. We are testing augmented reality glasses with embedded facial recognition capabilities together with Ntechlab company, which is known for creating the facial recognition tool FindFace. If the program is to proceed, however, then at a minimum:. In addition, in service to their customers, airlines should not partner with DHS in the future to conduct biometric screening of their passengers without first ensuring that DHS does all of the above, and without obtaining transparent and enforceable privacy, accuracy, and anti-bias guarantees from DHS. DHS initially tried to use fingerprint-based verification systems. These, however, disrupted traveler flow through airport terminals and were time-consuming and labor-intensive for administering personnel. If the traveler is rejected by the system, her credentials will be checked manually by a Customs and Border Protection CBP agent, or she will be subjected to another biometric check, such as a fingerprint comparison. For example, if John Doe were about to overstay his visa, he could ask a conspirator, John Roe, to leave the country using his passport. Visa overstay travel fraud could—in theory—be a problem worth solving. Foreign nationals who wish to remain in the country undetected past the expiration of their visas could be arranging to have others leave the country in their place using fraudulent credentials. But DHS has only ever published limited and anecdotal evidence of this. For example, one Immigration and Customs Enforcement ICE agent reportedly stated that the brother of a foreign national had traveled under his identity to generate a false exit record. Because the rationale for a biometric exit program is unclear, DHS has repeatedly expressed fundamental reservations about biometric exit. Instead of identifying these benefits, a senior DHS official paused, then responded tellingly: The program may exceed the authority granted to DHS by Congress because Congress has never explicitly authorized biometric collections from Americans at the border. Congress has passed legislation at least nine times concerning authorization for the collection of biometric data from foreign nationals, but no law directly authorizes DHS to collect the biometrics of Americans at the border. It never has. Without explicit authorization, DHS cannot and should not be scanning the faces of Americans as they depart on international flights, as it is currently doing. This is not the first time DHS has deployed a new privacy-invasive tool without conducting a required rulemaking process. In fact, a few years ago, under similar circumstances, a federal appeals court held that DHS was required to go through the rulemaking process before using body scanners at Transportation Security Administration TSA checkpoints. DHS must conduct a rulemaking because mandatory biometric screening, like the body scanners program, constitutes a policy with the force of law. Face scans are strictly mandatory for foreign nationals, and although DHS has said that face scans may be optional for some American citizens, it is unclear whether this is made known to American travelers. DHS has never measured the efficacy of airport face scans at catching impostors traveling with fraudulent credentials. Due to the challenges inherent to face recognition, it would be difficult for DHS to develop a system that is effective at catching every impostor without severely inconveniencing all other travelers. DHS currently measures performance based on how often the system correctly accepts travelers who are using true credentials. Yet DHS is not measuring that. As an analogy, consider a bouncer hired to check IDs at a bar. But the owner will almost certainly fire a bouncer who consistently allows entry to underage patrons using fake IDs. Like a bar owner who has not even asked how well a bouncer can identify fake IDs, DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. In fact, it may not be possible, given the current state of face recognition technology, to succeed on both of these fronts. There is an unavoidable trade-off between these two metrics: A system calibrated to reduce rejections of travelers using valid credentials will increase acceptance rates for impostors. Face recognition technology is not perfect. In reality, face recognition systems make mistakes on both of those fronts. A system may mistakenly reject a traveler flying under his own identity, for example, because his photo on file was taken four years prior and he has changed appearance since then. DHS clearly is focusing on making its face scan system minimally inconvenient for travelers using valid credentials. Indeed, analysis of face recognition algorithms indicates that some likely comparable systems would not perform very well at screening the type of impostor the system is likely to encounter: According to research conducted by the National Institute of Standards and Technology NIST , face recognition systems, like humans, have a harder time distinguishing among people who look alike. DHS indicated that it has been testing whether its face scanning system exhibits bias. Differential error rates could mean that innocent people will be pulled from the line at the boarding gate and subjected to manual fingerprinting at higher rates as a result of their complexion or gender. But because DHS has subsumed its evaluative process into a neutral-seeming computer algorithm, this bias may go undetected. Since February , NIST has tested more than 35 different face recognition algorithms designed to verify identities. Most face scanning algorithms function by first calculating the approximate similarity of two images presented for comparison, then accepting the presented images if the similarity calculation is greater than a predetermined match threshold, and rejecting the presented images if the calculation falls below the threshold. At the same time, the tested algorithms were more likely to mistakenly accept women, especially black women. Face recognition may perform differently as a result of variations in race or gender. The effects of these policies on free speech and association could be significant. DHS intends to subject every single traveler who departs for an international destination—American and foreign national alike—to biometric exit. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport. As DHS invests hundreds of millions of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader reaching and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports. But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action. The database includes 41 participants 23 women, 18 men. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions. The database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. The database is in the size of about 2. The database contains 3D face and hand scans. It was acquired using the structured light technology. According to our knowledge it is the first publicly available database where both sides of a hand were captured within one scan. Although there is a large amount of research examining the perception of emotional facial expressions, almost all of this research has focused on the perception of adult facial expressions. There are several excellent stimulus sets of adult facial expressions that can be easily obtained and used in scientific research i. However, there is no complete stimulus set of child affective facial expressions, and thus research on the perception of children making affective facial expression is sparse. In order to fully understand how humans respond to and process affective facial expressions, it is important to have this understanding across a variety of means. The Child Affective Facial Expressions Set CAFE is the first attempt to create a large and representative set of children making a variety of affective facial expressions that can be used for scientific research in this area. The set is made up of photographs of over child models ages making 7 different facial expressions - happy, angry, sad, fearful, surprise, neutral, and disgust. It is mainly intended to be used for benchmarking of the face identification methods, however it is possible to use this corpus in many related tasks e. Two different partitions of the database are available. The first one contains the cropped faces that were automatically extracted from the photographs using the Viola-Jones algorithm. The face size is thus almost uniform and the images contain just a small portion of background. The images in the second partition have more background, the face size also significantly differs and the faces are not localized. The purpose of this set is to evaluate and compare complete face recognition systems where the face detection and extraction is included. Each photograph is annotated with the name of a person. There are facial images for 13 IRTT students. They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of The actual size of cropped faces x and they are further resized to downscale factor 5. Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also. The database version 1. There are facial images for 10 IRTT girl students all are female with 10 faces per subject with age factor around 23 to 24 years. The colour images along with background are captured with a pixel resolution of x and their faces are cropped to x pixels. This IRTT student video database contains one video in. Later more videos will be included in this database. The video duration is This video is captured by smart phone. The faces and other features like eyes, lips and nose are extracted from this video separately. Part one is a set of color photographs that include a total of faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. The database is designed for providing high-quality HD multi-subject banchmarked video inputs for face recognition algorithms. The database is a useful input for offline as well as online Real-Time Video scenarios. It is harvested from Google image search. The dataset contains annotated cartoon faces of famous personalities of the world with varying profession. Additionally, we also provide real faces of the public figure to study cross modal retrieval tasks, such as, Photo2Cartoon retrieval. The IIIT-CFW can be used for the study spectrum of problems, such as, face synthesis, heterogeneous face recognition, cross modal retrieval, etc. Please use this database only for the academic research purpose. The database contains multiple face images of six stylized characters. The database contains facial expression images of six stylized characters. The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise. The dataset contains 3, images of 1, celebrities. Specs on Faces SoF Dataset. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions. The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions nose and mouth added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty easy, medium, and hard. That enlarges the number of images to be 42, images 26, male images and 16, female images. Furthermore, the dataset comes with a metadata that describes each subject from different aspects. The original images without filters or synthetic occlusions were captured in different countries over a long period. The data set is unrestricted, as such, it contains large pose, lighting, expression, race and age variation. It also contains images which are artistic impressions drawings, paintings etc. All images have size x pixels and are stored with jpeg compression. To simulate multiple scenarios, the images are captured with several facial variations, covering a range of emotions, actions, poses, illuminations, and occlusions. The database includes the raw light field images, 2D rendered images and associated depth maps, along with a rich set of metadata. Each subject is attempting to spoof a target identity. Hence this dataset consists of three sets of face images: The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of recordings were rated by adult participants. High levels of emotional validity and test-retest intrarater reliability were reported, as described in our PLoS One paper. All recordings are made freely available under a Creative Commons license, non-commerical license. Disguised Faces in the Wild. Face recognition research community has prepared several large-scale datasets captured in uncontrolled scenarios for performing face recognition. However, none of these focus on the specific challenge of face recognition under the disguise covariate. The proposed DFW dataset consists of 11, images of 1, subjects. The dataset contains a broad set of unconstrained disguised faces, taken from the Internet. The dataset encompasses several disguise variations with respect to hairstyles, beard, mustache, glasses, make-up, caps, hats, turbans, veils, masquerades and ball masks. This is coupled with other variations with respect to pose, lighting, expression, background, ethnicity, age, gender, clothing, hairstyles, and camera quality, thereby making the dataset challenging for the task of face recognition. The paper describing the database and the protocols is available here. In affective computing applications, access to labeled spontaneous affective data is essential for testing the designed algorithms under naturalistic and challenging conditions. Most databases available today are acted or do not contain audio data. BAUM-1 is a spontaneous audio-visual affective face database of affective and mental states. The video clips in the database are obtained by recording the subjects from the frontal view using a stereo camera and from the half-profile view using a mono camera. The subjects are first shown a sequence of images and short video clips, which are not only meticulously fashioned but also timed to evoke a set of emotions and mental states. Then, they express their ideas and feelings about the images and video clips they have watched in an unscripted and unguided way in Turkish. The target emotions, include the six basic ones happiness, anger, sadness, disgust, fear, surprise as well as boredom and contempt. We also target several mental states, which are unsure including confused, undecided , thinking, concentrating, and bothered. Baseline experimental results on the BAUM-1 database show that recognition of affective and mental states under naturalistic conditions is quite challenging. The database is expected to enable further research on audio-visual affect and mental state recognition under close-to-real scenarios. NMAPS is a database of human face images and their corresponding sketches generated using a novel approach implemented using Matlab tool. Images were taken under the random lighting conditions and environment with varying background and quality. Images captured under the varying conditions and quality mimic the real-world conditions and enables the researches to try out robust algorithms testing in the area of sketch generation and matching. This database is an unique contribution in the field of forensic science research as it contains the photo-sketch data-sets of South Indian people. The database was collected from 50 subjects of different age, sex and ethnicity, resulting a total of images. Variations include Expression, Pose, Occlusion and Illumination. Read more: SCfaceDB Landmarks The database is comprised of 21 facial landmarks from face images from users annotated manually by a human operator, as described in this paper. Multi-PIE A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The Yale Face Database B Contains single light source images of 10 subjects each seen under viewing conditions 9 poses x 64 illumination conditions. PIE Database, CMU A database of 41, images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions. Image Database of Facial Actions and Expressions - Expression Image Database 24 subjects are represented in this database, yielding between about 6 to 18 examples of the different requested actions. The University of Oulu Physics-Based Face Database Contains different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Face Video Database of the Max Planck Institute for Biological Cybernetics This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in at the Max Planck Institute for Biological Cybernetics. Caltech Faces face images. VALID Database With the aim to facilitate the development of robust audio, face, and multi-modal person recognition systems, the large and realistic multi-modal audio-visual VALID database was acquired in a noisy "real world" office scenario with no control on illumination or acoustic noise. Labeled Faces in the Wild Labeled Faces in the Wild is a database of face photographs designed for studying the problem of unconstrained face recognition. The Bosphorus Database The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. Plastic Surgery Face Database The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. Natural Visible and Infrared facial Expression database USTC-NVIE The database contains both spontaneous and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. Face recognition using photometric stereo This unique 3D face database is amongst the largest currently available, containing sessions of subjects, captured in two recording periods of approximately six months each. YouTube Faces Database The data set contains 3, videos of 1, different people. McGill Real-world Face Video Database This database contains video frames of x resolution from 60 video sequences, each of which recorded from a different subject 31 female and 29 male. The Adience image set and benchmark of unfiltered faces for age, gender and subject classification The dataset consists of 26, images, portraying 2, individuals, classified for 8 age groups, gender and including subject labels identity. FaceScrub - A Dataset With Over , Face Images of People Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data..

DHS must issue a Notice of Proposed Rulemaking, respond to public comments, and issue a Final Rule putting the public on notice about airport face scans and the rules that apply to them. DHS should Fully public facial Americans from any biometric exit Fully public facial. Congress has never explicitly authorized DHS to routinely scan the faces of U. Unless and until it receives a congressional mandate Fully public facial resume airport face scans of Americans, DHS should work to preserve and improve upon manual passport-face comparisons conducted at the TSA security checkpoint by TSA agents and at the boarding gate by gate agents.

DHS should study how well its face recognition technology works and publish the results. DHS should engage in an ongoing evaluation of both the effectiveness of its system at Fully public facial impostors under operational conditions and the rate at which its system falsely rejects individuals who are presenting their own valid credentials.

The use restrictions applicable to data collected by any airport face scan program should include an explicit prohibition on sharing collected data with state, local, or federal law enforcement without a warrant or lawfully issued court order. In service to their customers, airlines should not partner with DHS in the future to conduct biometric screening of travelers without first obtaining enforceable privacy, error rate, and guarantees against bias.

The airlines should also demand that DHS adopt a policy that, as detailed above, limits the use of data here by any airport face scanning program to the purpose for which it is collected. The airlines should ensure that all policies applicable to airport face scans are made Fully public facial available Fully public facial easily accessible to travelers.

And airlines should require that any updates or revisions to these policies be accompanied by a public notification of the alteration. These requirements should be paired with commitments from DHS to study and remedy system bias and to enhance system accuracy rates.

To that end, shareholders may wish to recommend via shareholder resolutions that the corporate boards adopt a policy prohibiting voluntary participation in Homeland Security biometric projects. It is just click for source. It is legally infirm.

It read article be technically flawed. And it Fully public facial implicate serious privacy concerns.

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If DHS persists with the program, significant reforms are vitally necessary. Critical guidance and close reading were provided by Professors Paul Ohm and David Vladeck, both of whom are Fully public facial faculty directors. The remainder of our expert reviewers will remain anonymous, but we are deeply thankful for their time and attention to this effort. We are particularly visit web page for additional support from the MacArthur Foundation that allowed us to successfully complete this report.

Harrison received his B. Before law school, Harrison worked as a paralegal at a law firm focusing on Fully public facial impacting consumer credit reporting agencies. Laura M. Laura completed her J. Alvaro M. Executive Summary.

See Board in a Snap: Customs and Border Protection Oct. Figure 1: A Fully public facial waits as his face is Fully public facial at Logan Airport in Boston Fully public facial to boarding a flight to Fully public facial. Boston Globe, all rights reserved.

Consolidated Appropriations Act, Pub. Senate, th Cong. If the program Fully public facial to proceed, however, then at a minimum: See infra Section B. It is fully annotated for the presence of AUs in videos event codingand partially coded on frame-level, indicating for each frame whether an AU is in either the neutral, onset, apex or offset phase. A small part was annotated for audio-visual laughters. It can be useful for research on topics such as multi-view face recognition, automatic lip reading and multi-modal speech recognition.

The dataset was recorded in 3 sessions, with a space of about a week between each session. In addition to the sentences, each person performed a head rotation sequence in each session. The sequence consists of the person moving their head to the left, right, back to the center, up, then down and finally return to center. The recording was done in an office environment using a broadcast quality digital video camera.

The video of each person is stored as a numbered sequence of JPEG images with a resolution of x pixels. The corresponding audio is stored as a mono, 16 bit, 32 kHz WAV file. Labeled Fully public facial in article source Wild. Labeled Faces in the Wild is Fully public facial database of face photographs designed for studying the problem of unconstrained face recognition.

The Fully public facial contains more than 13, images of faces collected from the web. Each face has been labeled with the name of the person pictured. The only constraint on these faces is that they were detected by the Viola-Jones face detector. Please see the database web page and the technical report linked there for more details.

Fully public facial

The LFWcrop Database. In the vast majority of images almost all of the background is omitted. LFWcrop was created due to concern about the misuse of the original LFW dataset, where face matching accuracy can be unrealistically boosted through the use of background parts of images i. As the location and Fully public facial of faces in LFW was determined through Fully public facial use of an automatic face locator detectorthe cropped faces Fully public facial LFWcrop exhibit real-life conditions, including mis-alignment, scale variations, in-plane as well as out-of-plane rotations.

The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Fully public facial data set, however, here we provide them after alignment https://punk.capitalcityfoundation.london/post139-siqal.php a commercial face alignment software.

Some of our results were produced using these images. We show this alignment to improve the performance of face recognition algorithms.

Valgure Sex Watch Video Wwxxvideo Com. It is fully annotated for the presence of AUs in videos event coding , and partially coded on frame-level, indicating for each frame whether an AU is in either the neutral, onset, apex or offset phase. A small part was annotated for audio-visual laughters. Sets of data taken from this database are available including high quality colour images, 32 KHz bit sound files, video sequences and a 3D model. Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions sun glasses and scarf. Contains different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Faces in frontal position captured under Horizon, Incandescent, Fluorescent and Daylight illuminant. Includes 3 spectral reflectance of skin per person measured from both cheeks and forehead. Contains RGB spectral response of camera used and spectral power distribution of illuminants. The goals to create the PEAL face database include: Each image has been rated on 6 emotion adjectives by 60 Japanese subjects. The dataset consists of gray level images with a resolution of x pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions. This is a collection of images useful for research in Psychology, such as sets of faces and objects. The images in the database are organised into SETS, with each set often representing a separate experimental study. The Sheffield Face Database previously: Consists of images of 20 people. Each covering a range of poses from profile to frontal views. Each subject exists in their own directory labelled 1a, 1b, The files are all in PGM format, approximately x pixels in shades of grey. This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in at the Max Planck Institute for Biological Cybernetics. The video cameras were arranged at 18 degrees intervals in a semi-circle around the subject at a distance of roughly 1. In order to facilitate the recovery of rigid head motion, the subject wore a headplate with 6 green markers. The website contains a total of video sequences in MPEG1 format. Caltech Faces. Human identification from facial features has been studied primarily using imagery from visible video cameras. Thermal imaging sensors are one of the most innovative emerging techonologies in the market. Fueled by ever lowering costs and improved sensitivity and resolution, our sensors provide exciting new oportunities for biometric identification. As part of our involvement in this effort, Equinox is collecting an extensive database of face imagery in the following modalities: This data collection is made available for experimentation and statistical performance evaluations. With the aim to facilitate the development of robust audio, face, and multi-modal person recognition systems, the large and realistic multi-modal audio-visual VALID database was acquired in a noisy "real world" office scenario with no control on illumination or acoustic noise. The database consists of five recording sessions of subjects over a period of one month. One session is recorded in a studio with controlled lighting and no background noise, the other 4 sessions are recorded in office type scenarios. The database has two parts. Part one contains colour pictures of faces having a high degree of variability in scale, location, orientation, pose, facial expression and lighting conditions, while part two has manually segmented results for each of the images in part one of the database. These images are acquired from a wide variety of sources such as digital cameras, pictures scanned using photo-scanner, other face databases and the World Wide Web. The database is intended for distribution to researchers. Georgia Tech Face Database. The database contains images of 50 people and is stored in JPEG format. Most of the images were taken in two different sessions to take into account the variations in illumination conditions, facial expression, and appearance. In addition to this, the faces were captured at different scales and orientations. Indian Face Database. There are eleven different images of each of 40 distinct subjects. For some subjects, some additional photographs are included. All the images were taken against a bright homogeneous background with the subjects in an upright, frontal position. The files are in JPEG format. The size of each image is x pixels, with grey levels per pixel. The images are organized in two main directories - males and females. In each of these directories, there are directories with name as a serial numbers, each corresponding to a single individual. In each of these directories, there are eleven different images of that subject, which have names of the form abc. The following orientations of the face are included: Available emotions are: The VidTIMIT database is comprised of video and corresponding audio recordings of 43 people, reciting short sentences. It can be useful for research on topics such as multi-view face recognition, automatic lip reading and multi-modal speech recognition. The dataset was recorded in 3 sessions, with a space of about a week between each session. In addition to the sentences, each person performed a head rotation sequence in each session. The sequence consists of the person moving their head to the left, right, back to the center, up, then down and finally return to center. The recording was done in an office environment using a broadcast quality digital video camera. The video of each person is stored as a numbered sequence of JPEG images with a resolution of x pixels. The corresponding audio is stored as a mono, 16 bit, 32 kHz WAV file. Labeled Faces in the Wild. Labeled Faces in the Wild is a database of face photographs designed for studying the problem of unconstrained face recognition. The database contains more than 13, images of faces collected from the web. Each face has been labeled with the name of the person pictured. The only constraint on these faces is that they were detected by the Viola-Jones face detector. Please see the database web page and the technical report linked there for more details. The LFWcrop Database. In the vast majority of images almost all of the background is omitted. LFWcrop was created due to concern about the misuse of the original LFW dataset, where face matching accuracy can be unrealistically boosted through the use of background parts of images i. As the location and size of faces in LFW was determined through the use of an automatic face locator detector , the cropped faces in LFWcrop exhibit real-life conditions, including mis-alignment, scale variations, in-plane as well as out-of-plane rotations. The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Wild data set, however, here we provide them after alignment using a commercial face alignment software. Some of our results were produced using these images. We show this alignment to improve the performance of face recognition algorithms. We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in the original image set. Note, however, that the images available here are grayscale versions of the originals. For each session, three shots were recorded with different but limited orientations of the head. Details about the population and typical problems affecting the quality are given in the referred link. The quality was limited but sufficient to show the ability of 3D face recognition. For privacy reasons, the texture images are not made available. In the period , this database has been downloaded by about researchers. A few papers present recognition results with the database like, of course, papers from the author. GavabDB is a 3D face database. It contains three-dimensional images of facial surfaces. These meshes correspond to 61 different individuals 45 male and 16 female having 9 images for each person. The total of the individuals are Caucasian and their age is between 18 and 40 years old. Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression. In particular, the 9 images corresponding to each individual are: This database is formed by up to subjects 75 men and 34 women , with 32 colour images per person. Each picture has a x pixel resolution, with the face occupying most of the image in an upright position. For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. In all cases, the background is plain and dark blue. The 32 images were classified in six groups according to the pose and lighting conditions: This database is delivered for free exclusively for research purposes. This database contains subjects, with approximately one woman every three men. If needed, the corresponding range data 2. Therefore, it is a multimodal database 2D, 2. During all time, a strict acquisition protocol was followed, with controlled lighting conditions. The person sat down on an adjustable stool opposite the scanner and in front of a blue wall. No glasses, hats or scarves were allowed. A total of 16 captures per person were taken in every session, with different poses and lighting conditions, trying to cover all possible variations, including turns in different directions, gestures and lighting changes. In every case only one parameter was modified between two captures. This is one of the main advantages of this database, respect to others. There are females and males in the database. Everyone has a 3D face data with neutral expression and without accessories. Original high-resolution 3D face data is acquired by the CyberWare 3D scanner in given environment, Every 3D face data has been preprocessed, and cut the redundant parts. Now the face database is available for research purpose only. The Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory in Beijing University of Technology is serving as the technical agent for distribution of the database and reserves the copyright of all the data in the database. The Bosphorus Database. The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from three aspects: Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. PUT Face Database. PUT Face Database consists of almost hi-res images of people. Images were taken in controlled conditions and the database is supplied with additional data including: Database is available for research purposes. The BFM consists of a generative 3D shape model covering the face surface from ear to ear and a high quality texture model. The model can be used either directly for 2D and 3D face recognition or to generate training and test images for any imaging condition. Hence, in addition to being a valuable model for face analysis it can also be viewed as a meta-database which allows the creation of accurately labeled synthetic training and testing images. The BFM web page additionally provides a set of registered scans of ten individuals, together with a set of renderings of these individuals with systematic pose and light variations. These scans are not included in the training set of the BFM and form a standardized test set with a ground truth for pose and illumination. Plastic Surgery Face Database. The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. It will be soon put into pilot operation on one route, with the driver and without passengers. A fully autonomous tram is expected by At the moment the Moscow Metro is analyzing international experience and searching for solutions suitable for Moscow. Beyond transportation, the area Lysenko wants to focus on is healthcare, an area where 5G and the IoT will bring wholesale change. Huawei CPE was used for the performance of remote ultrasound diagnostics and genetic sequencing. The test showed that response time was enough for comfortable remote work of the health specialists. Given its experience at the World Cup, Moscow has some real-world 5G data to help guide deployments and manage expectations. Theoretically, the 5G record in standard circumstances can be 35 Gbps, meaning less than three seconds to download an HD-movie. That said, testing in the U. Moscow still has the World Cup 5G equipment which will be reused in the pilots. To achieve this goal, we freely exchange ideas and experience in smart city development, including AI applications, with representatives of other cities worldwide. We broadly use public platforms like GitHub — to share our algorithms with developers around the world. According to research conducted by the National Institute of Standards and Technology NIST , face recognition systems, like humans, have a harder time distinguishing among people who look alike. DHS indicated that it has been testing whether its face scanning system exhibits bias. Differential error rates could mean that innocent people will be pulled from the line at the boarding gate and subjected to manual fingerprinting at higher rates as a result of their complexion or gender. But because DHS has subsumed its evaluative process into a neutral-seeming computer algorithm, this bias may go undetected. Since February , NIST has tested more than 35 different face recognition algorithms designed to verify identities. Most face scanning algorithms function by first calculating the approximate similarity of two images presented for comparison, then accepting the presented images if the similarity calculation is greater than a predetermined match threshold, and rejecting the presented images if the calculation falls below the threshold. At the same time, the tested algorithms were more likely to mistakenly accept women, especially black women. Face recognition may perform differently as a result of variations in race or gender. The effects of these policies on free speech and association could be significant. DHS intends to subject every single traveler who departs for an international destination—American and foreign national alike—to biometric exit. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport. As DHS invests hundreds of millions of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader reaching and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports. But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. Some airlines may well begin to explore ways to further monetize the technology they develop, for example by enhancing targeted advertising capabilities in airports. Some, if not all, of what airlines and technology partners may do with any data or technology to which they gain access through participation in biometric exit may be constrained by agreements with DHS. Without greater transparency regarding such private agreements and without substantive rules governing the role of private entities in the biometric exit process, there are few protections to ensure that biometric exit data and technology will not be abused. As it currently stands, the biometric exit program is unjustified. If the program is indeed designed to address visa overstay travel fraud, then DHS should study how often this type of fraud likely occurs, publish the results, and demonstrate that it is a problem worth solving. This could be done using data already available to the agency. For example, DHS could review historical data concerning the incidence of visa overstays entering U. DHS should suspend all airport face scans at departure gates until it comes into compliance with federal administrative law. As detailed above, the law requires DHS to solicit and consider comments from the public before adopting big-impact new programs like mandatory biometric scans. DHS must issue a Notice of Proposed Rulemaking, respond to public comments, and issue a Final Rule putting the public on notice about airport face scans and the rules that apply to them. DHS should exclude Americans from any biometric exit program. Congress has never explicitly authorized DHS to routinely scan the faces of U. Unless and until it receives a congressional mandate to resume airport face scans of Americans, DHS should work to preserve and improve upon manual passport-face comparisons conducted at the TSA security checkpoint by TSA agents and at the boarding gate by gate agents. DHS should study how well its face recognition technology works and publish the results. DHS should engage in an ongoing evaluation of both the effectiveness of its system at detecting impostors under operational conditions and the rate at which its system falsely rejects individuals who are presenting their own valid credentials. The use restrictions applicable to data collected by any airport face scan program should include an explicit prohibition on sharing collected data with state, local, or federal law enforcement without a warrant or lawfully issued court order. In service to their customers, airlines should not partner with DHS in the future to conduct biometric screening of travelers without first obtaining enforceable privacy, error rate, and guarantees against bias. The airlines should also demand that DHS adopt a policy that, as detailed above, limits the use of data collected by any airport face scanning program to the purpose for which it is collected. The airlines should ensure that all policies applicable to airport face scans are made publicly available and easily accessible to travelers. And airlines should require that any updates or revisions to these policies be accompanied by a public notification of the alteration. These requirements should be paired with commitments from DHS to study and remedy system bias and to enhance system accuracy rates. To that end, shareholders may wish to recommend via shareholder resolutions that the corporate boards adopt a policy prohibiting voluntary participation in Homeland Security biometric projects. It is unjustified. It is legally infirm. It may be technically flawed. And it may implicate serious privacy concerns. If DHS persists with the program, significant reforms are vitally necessary. Critical guidance and close reading were provided by Professors Paul Ohm and David Vladeck, both of whom are Center faculty directors. The remainder of our expert reviewers will remain anonymous, but we are deeply thankful for their time and attention to this effort. We are particularly grateful for additional support from the MacArthur Foundation that allowed us to successfully complete this report. Harrison received his B. Before law school, Harrison worked as a paralegal at a law firm focusing on issues impacting consumer credit reporting agencies. Laura M. Laura completed her J. Alvaro M..

We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in Fully public facial original image set. Note, however, that the images available here are grayscale versions of the originals. For each session, three shots were recorded with different but limited orientations of the Fully public facial.

Details about the population and typical problems affecting the quality are given in the referred link. The quality was limited but sufficient to show the ability of 3D face recognition. For privacy reasons, Fully public facial texture images are not made available. In the periodthis database has been downloaded by about researchers. A few papers present recognition results with the database like, of course, papers from the author.

GavabDB is a 3D face database. It contains three-dimensional images of facial surfaces. Fully public facial meshes correspond to 61 different individuals 45 male and 16 female having 9 images for each person. Click to see more total of the individuals are Caucasian and their age is between 18 and Fully public facial years old.

Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression. In particular, the 9 images corresponding to each individual Fully public facial This database is formed by up to subjects 75 men and 34 womenwith 32 colour images per person. Each picture More info a x pixel resolution, with the face occupying most of the image in an upright position.

For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. In all cases, the background is plain and dark blue. The 32 images were classified in six groups according to the pose and lighting conditions: This database is delivered for Fully public facial exclusively for research purposes. This database contains subjects, with approximately one woman Fully public facial three men.

If needed, the corresponding range data 2. Therefore, it is a multimodal database 2D, 2. During all time, a strict acquisition protocol was followed, with controlled lighting conditions. The person sat down on an adjustable stool opposite the scanner and in front of a blue wall. No glasses, hats or scarves were allowed.

A total of 16 captures per person were taken in every session, with different poses and lighting conditions, trying to cover all possible variations, including turns in different directions, gestures and lighting changes. In every case only one Fully public facial was modified between two captures. This is one of the main advantages of this database, respect to others.

Russia: Moscow's CIO Discusses Huawei 5G Risks, Facial Recognition And Driverless Cars

There are females and males in the database. Everyone has a 3D face data with neutral expression and without accessories. Original high-resolution 3D face data is acquired by the CyberWare 3D Fully public facial in given environment, Every Fully public facial face data has been preprocessed, and cut the redundant parts.

Now the face database is available Fully public facial research purpose only. The Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory in Beijing University of Technology is serving as the technical agent for distribution of the database and reserves the copyright of all the data in the database. The Bosphorus Database.

Villej Sexe Watch Video Livefreefun porn. It took us just two years to go from the first basic tests to a full-fledged public robotaxi service. Now, thanks to our agreement with Hyundai Mobis, we will be able to move even faster. After lab testing, it is possible to perform outdoor testing. So far the most successful is Yandex, whose autonomous car has already been tested on Moscow streets. About 30 other companies have the potential to start testing in the nearest future, including KAMAZ, whose autonomous bus and autonomous truck are now at the stage of lab testing. And it isn't just cars. The tram is now being tested in the depot. It will be soon put into pilot operation on one route, with the driver and without passengers. A fully autonomous tram is expected by At the moment the Moscow Metro is analyzing international experience and searching for solutions suitable for Moscow. Beyond transportation, the area Lysenko wants to focus on is healthcare, an area where 5G and the IoT will bring wholesale change. Huawei CPE was used for the performance of remote ultrasound diagnostics and genetic sequencing. For example, one Immigration and Customs Enforcement ICE agent reportedly stated that the brother of a foreign national had traveled under his identity to generate a false exit record. Because the rationale for a biometric exit program is unclear, DHS has repeatedly expressed fundamental reservations about biometric exit. Instead of identifying these benefits, a senior DHS official paused, then responded tellingly: The program may exceed the authority granted to DHS by Congress because Congress has never explicitly authorized biometric collections from Americans at the border. Congress has passed legislation at least nine times concerning authorization for the collection of biometric data from foreign nationals, but no law directly authorizes DHS to collect the biometrics of Americans at the border. It never has. Without explicit authorization, DHS cannot and should not be scanning the faces of Americans as they depart on international flights, as it is currently doing. This is not the first time DHS has deployed a new privacy-invasive tool without conducting a required rulemaking process. In fact, a few years ago, under similar circumstances, a federal appeals court held that DHS was required to go through the rulemaking process before using body scanners at Transportation Security Administration TSA checkpoints. DHS must conduct a rulemaking because mandatory biometric screening, like the body scanners program, constitutes a policy with the force of law. Face scans are strictly mandatory for foreign nationals, and although DHS has said that face scans may be optional for some American citizens, it is unclear whether this is made known to American travelers. DHS has never measured the efficacy of airport face scans at catching impostors traveling with fraudulent credentials. Due to the challenges inherent to face recognition, it would be difficult for DHS to develop a system that is effective at catching every impostor without severely inconveniencing all other travelers. DHS currently measures performance based on how often the system correctly accepts travelers who are using true credentials. Yet DHS is not measuring that. As an analogy, consider a bouncer hired to check IDs at a bar. But the owner will almost certainly fire a bouncer who consistently allows entry to underage patrons using fake IDs. Like a bar owner who has not even asked how well a bouncer can identify fake IDs, DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. In fact, it may not be possible, given the current state of face recognition technology, to succeed on both of these fronts. There is an unavoidable trade-off between these two metrics: A system calibrated to reduce rejections of travelers using valid credentials will increase acceptance rates for impostors. Face recognition technology is not perfect. In reality, face recognition systems make mistakes on both of those fronts. A system may mistakenly reject a traveler flying under his own identity, for example, because his photo on file was taken four years prior and he has changed appearance since then. DHS clearly is focusing on making its face scan system minimally inconvenient for travelers using valid credentials. Indeed, analysis of face recognition algorithms indicates that some likely comparable systems would not perform very well at screening the type of impostor the system is likely to encounter: According to research conducted by the National Institute of Standards and Technology NIST , face recognition systems, like humans, have a harder time distinguishing among people who look alike. DHS indicated that it has been testing whether its face scanning system exhibits bias. Differential error rates could mean that innocent people will be pulled from the line at the boarding gate and subjected to manual fingerprinting at higher rates as a result of their complexion or gender. But because DHS has subsumed its evaluative process into a neutral-seeming computer algorithm, this bias may go undetected. Since February , NIST has tested more than 35 different face recognition algorithms designed to verify identities. Most face scanning algorithms function by first calculating the approximate similarity of two images presented for comparison, then accepting the presented images if the similarity calculation is greater than a predetermined match threshold, and rejecting the presented images if the calculation falls below the threshold. At the same time, the tested algorithms were more likely to mistakenly accept women, especially black women. Face recognition may perform differently as a result of variations in race or gender. The effects of these policies on free speech and association could be significant. DHS intends to subject every single traveler who departs for an international destination—American and foreign national alike—to biometric exit. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport. As DHS invests hundreds of millions of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader reaching and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports. But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. Some airlines may well begin to explore ways to further monetize the technology they develop, for example by enhancing targeted advertising capabilities in airports. Some, if not all, of what airlines and technology partners may do with any data or technology to which they gain access through participation in biometric exit may be constrained by agreements with DHS. Without greater transparency regarding such private agreements and without substantive rules governing the role of private entities in the biometric exit process, there are few protections to ensure that biometric exit data and technology will not be abused. As it currently stands, the biometric exit program is unjustified. If the program is indeed designed to address visa overstay travel fraud, then DHS should study how often this type of fraud likely occurs, publish the results, and demonstrate that it is a problem worth solving. This could be done using data already available to the agency. For example, DHS could review historical data concerning the incidence of visa overstays entering U. DHS should suspend all airport face scans at departure gates until it comes into compliance with federal administrative law. As detailed above, the law requires DHS to solicit and consider comments from the public before adopting big-impact new programs like mandatory biometric scans. In particular, it contains recordings of the full temporal pattern of a facial expressions, from Neutral, through a series of onset, apex, and offset phases and back again to a neutral face. Recently recordings of naturalistic expressions have been added too. The database includes 41 participants 23 women, 18 men. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions. The database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. The database is in the size of about 2. The database contains 3D face and hand scans. It was acquired using the structured light technology. According to our knowledge it is the first publicly available database where both sides of a hand were captured within one scan. Although there is a large amount of research examining the perception of emotional facial expressions, almost all of this research has focused on the perception of adult facial expressions. There are several excellent stimulus sets of adult facial expressions that can be easily obtained and used in scientific research i. However, there is no complete stimulus set of child affective facial expressions, and thus research on the perception of children making affective facial expression is sparse. In order to fully understand how humans respond to and process affective facial expressions, it is important to have this understanding across a variety of means. The Child Affective Facial Expressions Set CAFE is the first attempt to create a large and representative set of children making a variety of affective facial expressions that can be used for scientific research in this area. The set is made up of photographs of over child models ages making 7 different facial expressions - happy, angry, sad, fearful, surprise, neutral, and disgust. It is mainly intended to be used for benchmarking of the face identification methods, however it is possible to use this corpus in many related tasks e. Two different partitions of the database are available. The first one contains the cropped faces that were automatically extracted from the photographs using the Viola-Jones algorithm. The face size is thus almost uniform and the images contain just a small portion of background. The images in the second partition have more background, the face size also significantly differs and the faces are not localized. The purpose of this set is to evaluate and compare complete face recognition systems where the face detection and extraction is included. Each photograph is annotated with the name of a person. There are facial images for 13 IRTT students. They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of The actual size of cropped faces x and they are further resized to downscale factor 5. Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also. The database version 1. There are facial images for 10 IRTT girl students all are female with 10 faces per subject with age factor around 23 to 24 years. The colour images along with background are captured with a pixel resolution of x and their faces are cropped to x pixels. This IRTT student video database contains one video in. Later more videos will be included in this database. The video duration is This video is captured by smart phone. The faces and other features like eyes, lips and nose are extracted from this video separately. Part one is a set of color photographs that include a total of faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. The database is designed for providing high-quality HD multi-subject banchmarked video inputs for face recognition algorithms. The database is a useful input for offline as well as online Real-Time Video scenarios. It is harvested from Google image search. The dataset contains annotated cartoon faces of famous personalities of the world with varying profession. Additionally, we also provide real faces of the public figure to study cross modal retrieval tasks, such as, Photo2Cartoon retrieval. The IIIT-CFW can be used for the study spectrum of problems, such as, face synthesis, heterogeneous face recognition, cross modal retrieval, etc. Please use this database only for the academic research purpose. The database contains multiple face images of six stylized characters. The database contains facial expression images of six stylized characters. The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise. The dataset contains 3, images of 1, celebrities. Specs on Faces SoF Dataset. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions. The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions nose and mouth added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty easy, medium, and hard. That enlarges the number of images to be 42, images 26, male images and 16, female images. Furthermore, the dataset comes with a metadata that describes each subject from different aspects. The original images without filters or synthetic occlusions were captured in different countries over a long period. The data set is unrestricted, as such, it contains large pose, lighting, expression, race and age variation. It also contains images which are artistic impressions drawings, paintings etc. All images have size x pixels and are stored with jpeg compression. To simulate multiple scenarios, the images are captured with several facial variations, covering a range of emotions, actions, poses, illuminations, and occlusions. The database includes the raw light field images, 2D rendered images and associated depth maps, along with a rich set of metadata. Each subject is attempting to spoof a target identity. Hence this dataset consists of three sets of face images: The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of recordings were rated by adult participants. High levels of emotional validity and test-retest intrarater reliability were reported, as described in our PLoS One paper. All recordings are made freely available under a Creative Commons license, non-commerical license. Disguised Faces in the Wild. Face recognition research community has prepared several large-scale datasets captured in uncontrolled scenarios for performing face recognition. However, none of these focus on the specific challenge of face recognition under the disguise covariate. The proposed DFW dataset consists of 11, images of 1, subjects. The dataset contains a broad set of unconstrained disguised faces, taken from the Internet. The dataset encompasses several disguise variations with respect to hairstyles, beard, mustache, glasses, make-up, caps, hats, turbans, veils, masquerades and ball masks. This is coupled with other variations with respect to pose, lighting, expression, background, ethnicity, age, gender, clothing, hairstyles, and camera quality, thereby making the dataset challenging for the task of face recognition. The paper describing the database and the protocols is available here. In affective computing applications, access to labeled spontaneous affective data is essential for testing the designed algorithms under naturalistic and challenging conditions. Most databases available today are acted or do not contain audio data. BAUM-1 is a spontaneous audio-visual affective face database of affective and mental states. The video clips in the database are obtained by recording the subjects from the frontal view using a stereo camera and from the half-profile view using a mono camera. The subjects are first shown a sequence of images and short video clips, which are not only meticulously fashioned but also timed to evoke a set of emotions and mental states. Then, they express their ideas and feelings about the images and video clips they have watched in an unscripted and unguided way in Turkish. The target emotions, include the six basic ones happiness, anger, sadness, disgust, fear, surprise as well as boredom and contempt. We also target several mental states, which are unsure including confused, undecided , thinking, concentrating, and bothered. Baseline experimental results on the BAUM-1 database show that recognition of affective and mental states under naturalistic conditions is quite challenging. The database is expected to enable further research on audio-visual affect and mental state recognition under close-to-real scenarios. NMAPS is a database of human face images and their corresponding sketches generated using a novel approach implemented using Matlab tool. Images were taken under the random lighting conditions and environment with varying background and quality. Images captured under the varying conditions and quality mimic the real-world conditions and enables the researches to try out robust algorithms testing in the area of sketch generation and matching. This database is an unique contribution in the field of forensic science research as it contains the photo-sketch data-sets of South Indian people. The database was collected from 50 subjects of different age, sex and ethnicity, resulting a total of images. Variations include Expression, Pose, Occlusion and Illumination. Read more: SCfaceDB Landmarks The database is comprised of 21 facial landmarks from face images from users annotated manually by a human operator, as described in this paper. Multi-PIE A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The Yale Face Database B Contains single light source images of 10 subjects each seen under viewing conditions 9 poses x 64 illumination conditions. PIE Database, CMU A database of 41, images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions. Image Database of Facial Actions and Expressions - Expression Image Database 24 subjects are represented in this database, yielding between about 6 to 18 examples of the different requested actions. The University of Oulu Physics-Based Face Database Contains different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Face Video Database of the Max Planck Institute for Biological Cybernetics This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in at the Max Planck Institute for Biological Cybernetics. Caltech Faces face images. VALID Database With the aim to facilitate the development of robust audio, face, and multi-modal person recognition systems, the large and realistic multi-modal audio-visual VALID database was acquired in a noisy "real world" office scenario with no control on illumination or acoustic noise. Labeled Faces in the Wild Labeled Faces in the Wild is a database of face photographs designed for studying the problem of unconstrained face recognition. The Bosphorus Database The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. Plastic Surgery Face Database The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. Natural Visible and Infrared facial Expression database USTC-NVIE The database contains both spontaneous and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. Face recognition using photometric stereo This unique 3D face database is amongst the largest currently available, containing sessions of subjects, captured in two recording periods of approximately six months each. YouTube Faces Database The data set contains 3, videos of 1, different people. McGill Real-world Face Video Database This database contains video frames of x resolution from 60 video sequences, each of which recorded from a different subject 31 female and 29 male. The Adience image set and benchmark of unfiltered faces for age, gender and subject classification The dataset consists of 26, images, portraying 2, individuals, classified for 8 age groups, gender and including subject labels identity. FaceScrub - A Dataset With Over , Face Images of People Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. LFW3D and Adience3D sets Frontalization is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos..

The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from source aspects: Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.

PUT Face Database. PUT Fully public facial Database consists of almost hi-res images of people. Images were taken in controlled conditions and the database is supplied with additional link including: Database is available for research purposes. The BFM consists of a generative 3D shape model covering the face surface from ear to ear and a high quality texture model.

The model can be used either directly for 2D and 3D face recognition or to generate training and test images for any imaging condition.

Hence, in addition to being a valuable model for face analysis it can also be viewed as a meta-database which allows the creation of accurately labeled synthetic training and testing images. The BFM web page additionally provides a set of registered scans of ten individuals, together with a set of renderings of these individuals with systematic pose and light variations.

These Fully public facial are not included in the training set of the BFM and form a standardized Fully public facial set with a ground truth for Fully public facial and illumination. Plastic Surgery Face Database. The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects.

Different types of facial Fully public facial surgeries have different impact on facial features. To enable the researchers to design and evaluate face recognition algorithms on all types of facial plastic surgeries, the database contains images from a wide variety of cases such as Rhinoplasty nose surgeryBlepharoplasty eyelid surgerybrow lift, skin peeling, and Rhytidectomy face lift.

For each individual, there are two frontal face images with proper illumination and neutral expression: The database contains image pairs corresponding to local surgeries and cases of global surgery e. The details of the database and performance evaluation of several well known face recognition algorithms is available in this paper.

IFDB is a Fully public facial database that can Fully public facial studies of the age classification systems. It Fully public facial over 3, color images.

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IFDB can be used for age classification, facial feature extraction, aging, facial ratio extraction, percent of facial similarity, facial surgery, race detection and other similar researches.

The NIR face image acquisition system consists of a camera, an LED light source, a filter, a Fully public facial grabber card and a computer. The active light source is in the NIR spectrum between nm - 1, nm. The peak wavelength is nm. The strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera face distance is between 80 cm Fully public facial cm, which is convenient for the users.

By using the data acquisition device described above, we collected NIR face images from subjects. Then the subject was asked to make expression and pose here and the corresponding images were collected.

To collect face images with scale variations, we asked the subjects to move near to or away from the Fully public facial in a certain range. At last, to collect face images with time variations, samples from 15 subjects were collected at two different times with an interval of more than two months.

Fully public facial each recording, we collected about images from each subject, and in total about 34, images were collected in the PolyU-NIRFD database.

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The indoor hyperspectral face acquisition system was built which mainly consists of a CRI's VariSpec LCTF and a Halogen Light, and includes a hyperspectral dataset of hyperspectral image Fully public facial from 25 volunteers with age range from 21 to 33 8 female and 17 male. For each individual, several sessions were collected with an average time space of 5 month. Fully public facial minimal interval is 3 months and the maximum is 10 months. Each session consists of three hyperspectral cubes - frontal, right and left views with neutral-expression.

The spectral range is from nm to nm with a step length of 10 nm, producing 33 bands in all. Since Fully public facial database was constructed over a long period of time, significant appearance variations of the subjects, e. In data collection, positions of the camera, light and subject are fixed, Fully public facial allows us to concentrate on the spectral characteristics for face recognition without masking from environmental changes. The database has a female-male ratio or nearly 1: This led to a diverse bi-modal database with both native and non-native English speakers.

Indin Xznxxx Watch Video Chilrun Xxx. The MMI Facial Expression Database is an ongoing project, that aims to deliver large volumes of visual data of facial expressions to the facial expression analysis community. A major issue hindering new developments in the area of Automatic Human Behaviour Analysis in general, and affect recognition in particular, is the lack of databases with displays of behaviour and affect. Now the face database is available for research purpose only. The Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory in Beijing University of Technology is serving as the technical agent for distribution of the database and reserves the copyright of all the data in the database. The Bosphorus Database. The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from three aspects: Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. PUT Face Database. PUT Face Database consists of almost hi-res images of people. Images were taken in controlled conditions and the database is supplied with additional data including: Database is available for research purposes. The BFM consists of a generative 3D shape model covering the face surface from ear to ear and a high quality texture model. The model can be used either directly for 2D and 3D face recognition or to generate training and test images for any imaging condition. Hence, in addition to being a valuable model for face analysis it can also be viewed as a meta-database which allows the creation of accurately labeled synthetic training and testing images. The BFM web page additionally provides a set of registered scans of ten individuals, together with a set of renderings of these individuals with systematic pose and light variations. These scans are not included in the training set of the BFM and form a standardized test set with a ground truth for pose and illumination. Plastic Surgery Face Database. The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. Different types of facial plastic surgeries have different impact on facial features. To enable the researchers to design and evaluate face recognition algorithms on all types of facial plastic surgeries, the database contains images from a wide variety of cases such as Rhinoplasty nose surgery , Blepharoplasty eyelid surgery , brow lift, skin peeling, and Rhytidectomy face lift. For each individual, there are two frontal face images with proper illumination and neutral expression: The database contains image pairs corresponding to local surgeries and cases of global surgery e. The details of the database and performance evaluation of several well known face recognition algorithms is available in this paper. IFDB is a large database that can support studies of the age classification systems. It contains over 3, color images. IFDB can be used for age classification, facial feature extraction, aging, facial ratio extraction, percent of facial similarity, facial surgery, race detection and other similar researches. The NIR face image acquisition system consists of a camera, an LED light source, a filter, a frame grabber card and a computer. The active light source is in the NIR spectrum between nm - 1, nm. The peak wavelength is nm. The strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera face distance is between 80 cm - cm, which is convenient for the users. By using the data acquisition device described above, we collected NIR face images from subjects. Then the subject was asked to make expression and pose changes and the corresponding images were collected. To collect face images with scale variations, we asked the subjects to move near to or away from the camera in a certain range. At last, to collect face images with time variations, samples from 15 subjects were collected at two different times with an interval of more than two months. In each recording, we collected about images from each subject, and in total about 34, images were collected in the PolyU-NIRFD database. The indoor hyperspectral face acquisition system was built which mainly consists of a CRI's VariSpec LCTF and a Halogen Light, and includes a hyperspectral dataset of hyperspectral image cubes from 25 volunteers with age range from 21 to 33 8 female and 17 male. For each individual, several sessions were collected with an average time space of 5 month. The minimal interval is 3 months and the maximum is 10 months. Each session consists of three hyperspectral cubes - frontal, right and left views with neutral-expression. The spectral range is from nm to nm with a step length of 10 nm, producing 33 bands in all. Since the database was constructed over a long period of time, significant appearance variations of the subjects, e. In data collection, positions of the camera, light and subject are fixed, which allows us to concentrate on the spectral characteristics for face recognition without masking from environmental changes. The database has a female-male ratio or nearly 1: This led to a diverse bi-modal database with both native and non-native English speakers. In total 12 sessions were captured for each client: The Phase I data consists of 21 questions with the question types ranging from: The Phase II data consists of 11 questions with the question types ranging from: The database was recorded using two mobile devices: The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone. The database is being made available by Dr. The images were acquired using a stereo imaging system at a high spatial resolution of 0. The color and range images were captured simultaneously and thus are perfectly registered to each other. All faces have been normalized to the frontal position and the tip of the nose is positioned at the center of the image. The images are of adult humans from all the major ethnic groups and both genders. These fiducial points were located manually on the facial color images using a computer based graphical user interface. Specific data partitions training, gallery, and probe that were employed at LIVE to develop the Anthropometric 3D Face Recognition algorithm are also available. The database contains both spontaneous and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database also includes expression images with and without glasses. FEI Face Database. There are 14 images for each of individuals, a total of images. All images are colourful and taken against a white homogenous background in an upright frontal position with profile rotation of up to about degrees. All faces are mainly represented by students and staff at FEI, between 19 and 40 years old with distinct appearance, hairstyle, and adorns. The number of male and female subjects are exactly the same and equal to An array of three cameras was placed above several portals natural choke points in terms of pedestrian traffic to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images ie. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal. The dataset consists of 25 subjects 19 male and 6 female in portal 1 and 29 subjects 23 male and 6 female in portal 2. In total, the dataset consists of 54 video sequences and 64, labelled face images. UMB database of 3D occluded faces. The database is available to universities and research centers interested in face detection, face recognition, face synthesis, etc. The main characteristics of VADANA, which distinguish it from current benchmarks, is the large number of intra-personal pairs order of thousand ; natural variations in pose, expression and illumination; and the rich set of additional meta-data provided along with standard partitions for direct comparison and bench-marking efforts. MORPH database is the largest publicly available longitudinal face database. The MORPH database contains 55, images of more than 13, people within the age ranges of 16 to There are an average of 4 images per individual with the time span between each image being an average of days. This data set was comprised for research on facial analytics and facial recognition. Face images of subjects 70 males and 30 females were captured; for each subject one image was captured at each distance in daytime and nighttime. All the images of individual subjects are frontal faces without glasses and collected in a single sitting. Face recognition using photometric stereo. This unique 3D face database is amongst the largest currently available, containing sessions of subjects, captured in two recording periods of approximately six months each. The Photoface device was located in an unsupervised corridor allowing real-world and unconstrained capture. Each session comprises four differently lit colour photographs of the subject, from which surface normal and albedo estimations can be calculated photometric stereo Matlab code implementation included. This allows for many testing scenarios and data fusion modalities. Eleven facial landmarks have been manually located on each session for alignment purposes. Additionally, the Photoface Query Tool is supplied implemented in Matlab , which allows for subsets of the database to be extracted according to selected metadata e. The Dataset consists of multimodal facial images of 52 people 14 females, 38 males acquired with a Kinect sensor. The data is captured in two sessions at different intervals of about two weeks. In each session, 9 facial images are collected from each person according to different facial expressions, lighting and occlusion conditions: An RGB color image, a depth map provided both as a bitmap depth image and a text file containing the original depth levels sensed by Kinect as well as the associated 3D data are provided for all samples. In addition, the dataset includes 6 manually labeled landmark positions for every face: YouTube Faces Database. The data set contains 3, videos of 1, different people. All the videos were downloaded from YouTube. An average of 2. The shortest clip duration is 48 frames, the longest clip is 6, frames, and the average length of a video clip is In designing our video data set and benchmarks we follow the example of the 'Labeled Faces in the Wild' LFW image collection. Specifically, our goal is to produce a large scale collection of videos along with labels indicating the identities of a person appearing in each video. In addition, we publish benchmark tests, intended to measure the performance of video pair-matching techniques on these videos. Finally, we provide descriptor encodings for the faces appearing in these videos, using well established descriptor methods. The dataset consists of subjects, specifically Caucasian females, from YouTube makeup tutorials. Images of the subjects before and after the application of makeup were captured. There are four shots per subject: For a few subjects, three shots each before and after the application of makeup were obtained. The makeup in these face images varies from subtle to heavy. The cosmetic alteration is mainly in the ocular area, where the eyes have been accentuated by diverse eye makeup products. Additional changes are on the quality of the skin due to the application of foundation and change in lip color. This dataset includes some variations in expression and pose. The illumination condition is reasonably constant over multiple shots of the same subject. In few cases, the hair style before and after makeup changes drastically. We added makeup by using a publicly available tool from Taaz. Three virtual makeovers were created: Hence, the assembled dataset contains four images per subject: MIW Makeup in the "wild" Dataset. The MIW dataset contains subjects with images per subject. Total number of images is 77 with makeup and 77 without makeup. The images are obtained from the internet and the faces are unconstrained. It currently contains frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. Each frame consists of: The data is collected in 3 different sessions for all subjects and for each session 5 videos of frames are captured. The recordings are done under controlled conditions, with frontal-view and neutral expression. In the third session, 3D mask attacks are captured by a single operator attacker. If you use this database please cite this publication: Erdogmus and S. Source code to reproduce experiments in the paper: This year, we intend to increase the number to ,, meaning all the cameras installed in public areas to optimize the use of the system. The system captures 1. But it isn't just CCTV. Remember the stories put out by China's PR machine, of police officers equipped with AI smart-glasses? Well, Moscow plans the same. FindFace is a highly accurate facial recognition engine, the standout in a country known for its prowess in the space. But Lysenko also talks of international collaboration, with China being the obvious place to start. Of course, we are interested in exchanging experience with our foreign colleagues, including China, which has advanced in the deployment of the facial recognition system more than other countries. Which brings the discussion to 5G. Moscow has already played around with 5G and plans full-scale pilots through Fifty people used VR glasses to watch a broadcast. Stadium cameras broadcast to a 5G cell tower, and the cell tower transmitted to smartphones connected to the VR glasses, using up to 35 Mbps per device. These requirements should be paired with commitments from DHS to study and remedy system bias and to enhance system accuracy rates. To that end, shareholders may wish to recommend via shareholder resolutions that the corporate boards adopt a policy prohibiting voluntary participation in Homeland Security biometric projects. It is unjustified. It is legally infirm. It may be technically flawed. And it may implicate serious privacy concerns. If DHS persists with the program, significant reforms are vitally necessary. Critical guidance and close reading were provided by Professors Paul Ohm and David Vladeck, both of whom are Center faculty directors. The remainder of our expert reviewers will remain anonymous, but we are deeply thankful for their time and attention to this effort. We are particularly grateful for additional support from the MacArthur Foundation that allowed us to successfully complete this report. Harrison received his B. Before law school, Harrison worked as a paralegal at a law firm focusing on issues impacting consumer credit reporting agencies. Laura M. Laura completed her J. Alvaro M. Executive Summary. See Board in a Snap: Customs and Border Protection Oct. Figure 1: A man waits as his face is scanned at Logan Airport in Boston prior to boarding a flight to Aruba. Boston Globe, all rights reserved. Consolidated Appropriations Act, Pub. Senate, th Cong. If the program is to proceed, however, then at a minimum: See infra Section B. See 5 U. According to the latest data available, taking the extra time to process rejected passengers could translate into more time spent on the tarmac for every traveler. When CBP deployed a fingerprint-based biometric solution, it took six CBP officers 45 minutes to collect fingerprints from 75 passengers. This is equivalent to approximately 3. CBP has never suggested that more than one CBP officer or airline gate agent will fingerprint rejected travelers. If all of these rejected travelers are foreign nationals, then according to the GAO report, it would take 3. At that rate, assuming that false rejections occur at a regular interval and are not back-loaded, which would delay boarding even further , CBP would need more than 50 minutes from the beginning of boarding just to screen rejected travelers. Separately, approximately 40, passengers depart on international flights from JFK each day. At Boston Logan International Airport, , international travelers deplaned or boarded during the month of January If half of those passengers were outbound departures, , passengers in January—or 7, passengers each day—departed from Boston Logan International Airport. At a rate of 1 in 25, that would mean passengers would be wrongfully denied boarding at Logan Airport on a daily basis. See infra Section C. See infra Section D. DHS should justify its investment in face scans by supplying evidence of the problem it purportedly solves. DHS should stop scanning the faces of American citizens as they leave the country. DHS should prove that airport face scans are capable of identifying impostors without inconveniencing everyone else. DHS should adopt a public policy that prohibits secondary uses of the data collected by its airport face scan program. DHS should provide fairness and privacy guarantees to the airlines with which it partners. Figure 2: A traveler has his face scanned as a Customs and Border Protection agent provides instruction. Associated Press, all rights reserved. Sidebar 1: What Is Biometric Exit? Partner Process 4 June 12, , https: Because accuracy is highly dependent on image quality, the inclusion of photos from sources other than passport and visa databases, such as law enforcement encounters, likely lowers overall system accuracy rates beyond what is assumed in this paper. Partner Process, supra note 4, at 3—4. See id..

In total 12 sessions were captured for each client: The Phase I data consists of link questions with the question types ranging from: The Phase II data consists of 11 questions with the question types ranging from: The database was recorded using two mobile devices: The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone.

The database is being made available by Dr. The images were acquired using a stereo imaging system at a high spatial resolution of 0. The color and range images were captured simultaneously and thus are perfectly registered to each other. All faces have Fully public facial normalized to the frontal position and the tip of the nose is positioned at the center of the image. The images are of adult humans from all the major ethnic groups and both genders.

These fiducial points were Fully public facial manually on the facial color images using a computer based graphical user interface. Specific Fully public facial partitions training, gallery, and probe that were employed at LIVE to develop the Anthropometric 3D Face Recognition algorithm Fully public facial also available.

The database contains both Fully public facial and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database also includes expression images with and without glasses.

FEI Face Database. There are 14 images for each of individuals, a total of images. All images are colourful and taken against a white homogenous Fully public facial in an upright frontal position with profile rotation of up to about degrees. All faces are mainly represented by students and staff at FEI, between 19 and 40 Fully public facial old with distinct appearance, hairstyle, and adorns.

The number of male and female subjects are exactly the same and equal to An array of three cameras was placed above several portals natural choke points in terms of pedestrian traffic to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images ie.

Due to the three camera configuration, one of the cameras is likely to capture a face set where Fully public facial subset of the faces is near-frontal. The dataset consists of 25 subjects 19 male and 6 female in portal 1 and 29 subjects 23 male and 6 female in portal 2. Fully public facial total, the dataset consists of 54 video sequences and 64, labelled Fully public facial images. UMB database of 3D occluded faces.

Kendra s thanksgiving stuffing brazzers. When benchmarking an click it is recommendable to use https://jacuzzi.capitalcityfoundation.london/post8672-rilaga.php standard test data set for researchers to be able to directly compare the results.

While there are many databases in use currently, Fully public facial choice of an appropriate database to be used should be made based on the task given Fully public facial, expressions, lighting etc.

Another now Get fucked is to choose the data set specific to the property to be tested e. Li and Anil K. Jain, ed.

To the best of our knowledge this is the first available benchmark that directly assesses the accuracy of algorithms to automatically verify the compliance of face images to the ISO standard, in the attempt of semi-automating the document issuing process.

Solveig porn Watch Video Hotfilth tumblr. Six cameras capture human faces from three different angles. Three out of the six cameras have smaller focus length, and the other three have larger focus length. Plan to capture subjects in 3 sessions in different time period. For one session, both in-door and out-door scenario will be captured. User-dependent pose and expression variation are expected from the video sequences. Ten different images of each of 40 distinct subjects. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position with tolerance for some side movement. They ranged in age from 18 to 30 years. Sixty-five percent were female, 15 percent were African-American, and three percent were Asian or Latino. Subjects were instructed by an experimenter to perform a series of 23 facial displays that included single action units and combinations of action units. Image sequences from neutral to target display were digitized into by or pixel arrays with 8-bit precision for grayscale values. Included with the image files are "sequence" files; these are short text files that describe the order in which images should be read. It provides two training sets: High resolution pictures, including frontal, half-profile and profile view; 2. The head models were generated by fitting a morphable model to the high-resolution training images. The 3D models are not included in the database. The test set consists of images per subject. We varied the illumination, pose up to about 30 degrees of rotation in depth and the background. Thus, about 7, color images are included in the database, and each has a matching gray scale image used in the neural network analysis. Contains images of people of various racial origins, mainly of first year undergraduate students, so the majority of indivuals are between years old but some older individuals are also present. Some individuals are wearing glasses and beards. There are images of individuals cases male and 78 female. The database contains both front and side profile views when available. Separating front views and profiles, there are cases with two or more front views and with only one front view. Profiles have 89 cases with two or more profiles and with only one profile. Cases with both fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more fronts and one profile, and with only one front and one profile. JPEG format. Database is made up from 37 different faces and provides 5 shots for each person. These shots were taken at one week intervals or when drastic face changes occurred in the meantime. Also, they have been asked to rotate the head once again without glasses if they wear any. Contains four recordings of subjects taken over a period of four months. Each recording contains a speaking head shot and a rotating head shot. Sets of data taken from this database are available including high quality colour images, 32 KHz bit sound files, video sequences and a 3D model. Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions sun glasses and scarf. Contains different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Faces in frontal position captured under Horizon, Incandescent, Fluorescent and Daylight illuminant. Includes 3 spectral reflectance of skin per person measured from both cheeks and forehead. Contains RGB spectral response of camera used and spectral power distribution of illuminants. The goals to create the PEAL face database include: Each image has been rated on 6 emotion adjectives by 60 Japanese subjects. The dataset consists of gray level images with a resolution of x pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions. This is a collection of images useful for research in Psychology, such as sets of faces and objects. The images in the database are organised into SETS, with each set often representing a separate experimental study. The Sheffield Face Database previously: Consists of images of 20 people. Each covering a range of poses from profile to frontal views. Each subject exists in their own directory labelled 1a, 1b, The files are all in PGM format, approximately x pixels in shades of grey. This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in at the Max Planck Institute for Biological Cybernetics. The video cameras were arranged at 18 degrees intervals in a semi-circle around the subject at a distance of roughly 1. In order to facilitate the recovery of rigid head motion, the subject wore a headplate with 6 green markers. The website contains a total of video sequences in MPEG1 format. Caltech Faces. Human identification from facial features has been studied primarily using imagery from visible video cameras. Thermal imaging sensors are one of the most innovative emerging techonologies in the market. Fueled by ever lowering costs and improved sensitivity and resolution, our sensors provide exciting new oportunities for biometric identification. As part of our involvement in this effort, Equinox is collecting an extensive database of face imagery in the following modalities: This data collection is made available for experimentation and statistical performance evaluations. With the aim to facilitate the development of robust audio, face, and multi-modal person recognition systems, the large and realistic multi-modal audio-visual VALID database was acquired in a noisy "real world" office scenario with no control on illumination or acoustic noise. The database consists of five recording sessions of subjects over a period of one month. One session is recorded in a studio with controlled lighting and no background noise, the other 4 sessions are recorded in office type scenarios. The database has two parts. Part one contains colour pictures of faces having a high degree of variability in scale, location, orientation, pose, facial expression and lighting conditions, while part two has manually segmented results for each of the images in part one of the database. These images are acquired from a wide variety of sources such as digital cameras, pictures scanned using photo-scanner, other face databases and the World Wide Web. The database is intended for distribution to researchers. Georgia Tech Face Database. The database contains images of 50 people and is stored in JPEG format. Most of the images were taken in two different sessions to take into account the variations in illumination conditions, facial expression, and appearance. In addition to this, the faces were captured at different scales and orientations. Indian Face Database. There are eleven different images of each of 40 distinct subjects. For some subjects, some additional photographs are included. All the images were taken against a bright homogeneous background with the subjects in an upright, frontal position. The files are in JPEG format. The size of each image is x pixels, with grey levels per pixel. The images are organized in two main directories - males and females. In each of these directories, there are directories with name as a serial numbers, each corresponding to a single individual. In each of these directories, there are eleven different images of that subject, which have names of the form abc. The following orientations of the face are included: Available emotions are: The VidTIMIT database is comprised of video and corresponding audio recordings of 43 people, reciting short sentences. It can be useful for research on topics such as multi-view face recognition, automatic lip reading and multi-modal speech recognition. The dataset was recorded in 3 sessions, with a space of about a week between each session. In addition to the sentences, each person performed a head rotation sequence in each session. The sequence consists of the person moving their head to the left, right, back to the center, up, then down and finally return to center. The recording was done in an office environment using a broadcast quality digital video camera. The video of each person is stored as a numbered sequence of JPEG images with a resolution of x pixels. The corresponding audio is stored as a mono, 16 bit, 32 kHz WAV file. Labeled Faces in the Wild. Labeled Faces in the Wild is a database of face photographs designed for studying the problem of unconstrained face recognition. The database contains more than 13, images of faces collected from the web. Each face has been labeled with the name of the person pictured. The only constraint on these faces is that they were detected by the Viola-Jones face detector. Please see the database web page and the technical report linked there for more details. The LFWcrop Database. In the vast majority of images almost all of the background is omitted. LFWcrop was created due to concern about the misuse of the original LFW dataset, where face matching accuracy can be unrealistically boosted through the use of background parts of images i. As the location and size of faces in LFW was determined through the use of an automatic face locator detector , the cropped faces in LFWcrop exhibit real-life conditions, including mis-alignment, scale variations, in-plane as well as out-of-plane rotations. The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Wild data set, however, here we provide them after alignment using a commercial face alignment software. Some of our results were produced using these images. We show this alignment to improve the performance of face recognition algorithms. We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in the original image set. Note, however, that the images available here are grayscale versions of the originals. For each session, three shots were recorded with different but limited orientations of the head. Details about the population and typical problems affecting the quality are given in the referred link. The quality was limited but sufficient to show the ability of 3D face recognition. For privacy reasons, the texture images are not made available. In the period , this database has been downloaded by about researchers. A few papers present recognition results with the database like, of course, papers from the author. GavabDB is a 3D face database. It contains three-dimensional images of facial surfaces. These meshes correspond to 61 different individuals 45 male and 16 female having 9 images for each person. The total of the individuals are Caucasian and their age is between 18 and 40 years old. Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression. In particular, the 9 images corresponding to each individual are: This database is formed by up to subjects 75 men and 34 women , with 32 colour images per person. Each picture has a x pixel resolution, with the face occupying most of the image in an upright position. For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. Foreign nationals who wish to remain in the country undetected past the expiration of their visas could be arranging to have others leave the country in their place using fraudulent credentials. But DHS has only ever published limited and anecdotal evidence of this. For example, one Immigration and Customs Enforcement ICE agent reportedly stated that the brother of a foreign national had traveled under his identity to generate a false exit record. Because the rationale for a biometric exit program is unclear, DHS has repeatedly expressed fundamental reservations about biometric exit. Instead of identifying these benefits, a senior DHS official paused, then responded tellingly: The program may exceed the authority granted to DHS by Congress because Congress has never explicitly authorized biometric collections from Americans at the border. Congress has passed legislation at least nine times concerning authorization for the collection of biometric data from foreign nationals, but no law directly authorizes DHS to collect the biometrics of Americans at the border. It never has. Without explicit authorization, DHS cannot and should not be scanning the faces of Americans as they depart on international flights, as it is currently doing. This is not the first time DHS has deployed a new privacy-invasive tool without conducting a required rulemaking process. In fact, a few years ago, under similar circumstances, a federal appeals court held that DHS was required to go through the rulemaking process before using body scanners at Transportation Security Administration TSA checkpoints. DHS must conduct a rulemaking because mandatory biometric screening, like the body scanners program, constitutes a policy with the force of law. Face scans are strictly mandatory for foreign nationals, and although DHS has said that face scans may be optional for some American citizens, it is unclear whether this is made known to American travelers. DHS has never measured the efficacy of airport face scans at catching impostors traveling with fraudulent credentials. Due to the challenges inherent to face recognition, it would be difficult for DHS to develop a system that is effective at catching every impostor without severely inconveniencing all other travelers. DHS currently measures performance based on how often the system correctly accepts travelers who are using true credentials. Yet DHS is not measuring that. As an analogy, consider a bouncer hired to check IDs at a bar. But the owner will almost certainly fire a bouncer who consistently allows entry to underage patrons using fake IDs. Like a bar owner who has not even asked how well a bouncer can identify fake IDs, DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. In fact, it may not be possible, given the current state of face recognition technology, to succeed on both of these fronts. There is an unavoidable trade-off between these two metrics: A system calibrated to reduce rejections of travelers using valid credentials will increase acceptance rates for impostors. Face recognition technology is not perfect. In reality, face recognition systems make mistakes on both of those fronts. A system may mistakenly reject a traveler flying under his own identity, for example, because his photo on file was taken four years prior and he has changed appearance since then. DHS clearly is focusing on making its face scan system minimally inconvenient for travelers using valid credentials. Indeed, analysis of face recognition algorithms indicates that some likely comparable systems would not perform very well at screening the type of impostor the system is likely to encounter: According to research conducted by the National Institute of Standards and Technology NIST , face recognition systems, like humans, have a harder time distinguishing among people who look alike. DHS indicated that it has been testing whether its face scanning system exhibits bias. Differential error rates could mean that innocent people will be pulled from the line at the boarding gate and subjected to manual fingerprinting at higher rates as a result of their complexion or gender. But because DHS has subsumed its evaluative process into a neutral-seeming computer algorithm, this bias may go undetected. Since February , NIST has tested more than 35 different face recognition algorithms designed to verify identities. Most face scanning algorithms function by first calculating the approximate similarity of two images presented for comparison, then accepting the presented images if the similarity calculation is greater than a predetermined match threshold, and rejecting the presented images if the calculation falls below the threshold. At the same time, the tested algorithms were more likely to mistakenly accept women, especially black women. Face recognition may perform differently as a result of variations in race or gender. The effects of these policies on free speech and association could be significant. DHS intends to subject every single traveler who departs for an international destination—American and foreign national alike—to biometric exit. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport. As DHS invests hundreds of millions of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader reaching and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports. But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. Some airlines may well begin to explore ways to further monetize the technology they develop, for example by enhancing targeted advertising capabilities in airports. Some, if not all, of what airlines and technology partners may do with any data or technology to which they gain access through participation in biometric exit may be constrained by agreements with DHS. Without greater transparency regarding such private agreements and without substantive rules governing the role of private entities in the biometric exit process, there are few protections to ensure that biometric exit data and technology will not be abused. As it currently stands, the biometric exit program is unjustified. If the program is indeed designed to address visa overstay travel fraud, then DHS should study how often this type of fraud likely occurs, publish the results, and demonstrate that it is a problem worth solving. This could be done using data already available to the agency. For example, DHS could review historical data concerning the incidence of visa overstays entering U. After lab testing, it is possible to perform outdoor testing. So far the most successful is Yandex, whose autonomous car has already been tested on Moscow streets. About 30 other companies have the potential to start testing in the nearest future, including KAMAZ, whose autonomous bus and autonomous truck are now at the stage of lab testing. And it isn't just cars. The tram is now being tested in the depot. It will be soon put into pilot operation on one route, with the driver and without passengers. A fully autonomous tram is expected by At the moment the Moscow Metro is analyzing international experience and searching for solutions suitable for Moscow. Beyond transportation, the area Lysenko wants to focus on is healthcare, an area where 5G and the IoT will bring wholesale change. Huawei CPE was used for the performance of remote ultrasound diagnostics and genetic sequencing. The test showed that response time was enough for comfortable remote work of the health specialists. Given its experience at the World Cup, Moscow has some real-world 5G data to help guide deployments and manage expectations. A major issue hindering new developments in the area of Automatic Human Behaviour Analysis in general, and affect recognition in particular, is the lack of databases with displays of behaviour and affect. To address this problem, the MMI-Facial Expression database was conceived in as a resource for building and evaluating facial expression recognition algorithms..

Jonathon Phillips, A. Martin, C. Wilson, M. Mansfield, J. Delac, M. Grgic, S. The FERET program set out to establish a large database of facial images that was gathered independently from the algorithm developers. Harry Wechsler at George Mason University was selected to direct the collection of this database. The database collection was a collaborative effort Fully public facial Dr.

Fully public facial

Madras Girlsex Watch Video Pornstart. A fully autonomous tram is expected by At the moment the Moscow Metro is analyzing international experience and searching for solutions suitable for Moscow. Beyond transportation, the area Lysenko wants to focus on is healthcare, an area where 5G and the IoT will bring wholesale change. Huawei CPE was used for the performance of remote ultrasound diagnostics and genetic sequencing. The test showed that response time was enough for comfortable remote work of the health specialists. Given its experience at the World Cup, Moscow has some real-world 5G data to help guide deployments and manage expectations. Theoretically, the 5G record in standard circumstances can be 35 Gbps, meaning less than three seconds to download an HD-movie. That said, testing in the U. Moscow still has the World Cup 5G equipment which will be reused in the pilots. To achieve this goal, we freely exchange ideas and experience in smart city development, including AI applications, with representatives of other cities worldwide. We broadly use public platforms like GitHub — to share our algorithms with developers around the world. The response of those citizens to , facial recognition cameras though, citywide, remains to be seen. Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression. In particular, the 9 images corresponding to each individual are: This database is formed by up to subjects 75 men and 34 women , with 32 colour images per person. Each picture has a x pixel resolution, with the face occupying most of the image in an upright position. For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. In all cases, the background is plain and dark blue. The 32 images were classified in six groups according to the pose and lighting conditions: This database is delivered for free exclusively for research purposes. This database contains subjects, with approximately one woman every three men. If needed, the corresponding range data 2. Therefore, it is a multimodal database 2D, 2. During all time, a strict acquisition protocol was followed, with controlled lighting conditions. The person sat down on an adjustable stool opposite the scanner and in front of a blue wall. No glasses, hats or scarves were allowed. A total of 16 captures per person were taken in every session, with different poses and lighting conditions, trying to cover all possible variations, including turns in different directions, gestures and lighting changes. In every case only one parameter was modified between two captures. This is one of the main advantages of this database, respect to others. There are females and males in the database. Everyone has a 3D face data with neutral expression and without accessories. Original high-resolution 3D face data is acquired by the CyberWare 3D scanner in given environment, Every 3D face data has been preprocessed, and cut the redundant parts. Now the face database is available for research purpose only. The Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory in Beijing University of Technology is serving as the technical agent for distribution of the database and reserves the copyright of all the data in the database. The Bosphorus Database. The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from three aspects: Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. PUT Face Database. PUT Face Database consists of almost hi-res images of people. Images were taken in controlled conditions and the database is supplied with additional data including: Database is available for research purposes. The BFM consists of a generative 3D shape model covering the face surface from ear to ear and a high quality texture model. The model can be used either directly for 2D and 3D face recognition or to generate training and test images for any imaging condition. Hence, in addition to being a valuable model for face analysis it can also be viewed as a meta-database which allows the creation of accurately labeled synthetic training and testing images. The BFM web page additionally provides a set of registered scans of ten individuals, together with a set of renderings of these individuals with systematic pose and light variations. These scans are not included in the training set of the BFM and form a standardized test set with a ground truth for pose and illumination. Plastic Surgery Face Database. The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. Different types of facial plastic surgeries have different impact on facial features. To enable the researchers to design and evaluate face recognition algorithms on all types of facial plastic surgeries, the database contains images from a wide variety of cases such as Rhinoplasty nose surgery , Blepharoplasty eyelid surgery , brow lift, skin peeling, and Rhytidectomy face lift. For each individual, there are two frontal face images with proper illumination and neutral expression: The database contains image pairs corresponding to local surgeries and cases of global surgery e. The details of the database and performance evaluation of several well known face recognition algorithms is available in this paper. IFDB is a large database that can support studies of the age classification systems. It contains over 3, color images. IFDB can be used for age classification, facial feature extraction, aging, facial ratio extraction, percent of facial similarity, facial surgery, race detection and other similar researches. The NIR face image acquisition system consists of a camera, an LED light source, a filter, a frame grabber card and a computer. The active light source is in the NIR spectrum between nm - 1, nm. The peak wavelength is nm. The strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera face distance is between 80 cm - cm, which is convenient for the users. By using the data acquisition device described above, we collected NIR face images from subjects. Then the subject was asked to make expression and pose changes and the corresponding images were collected. To collect face images with scale variations, we asked the subjects to move near to or away from the camera in a certain range. At last, to collect face images with time variations, samples from 15 subjects were collected at two different times with an interval of more than two months. In each recording, we collected about images from each subject, and in total about 34, images were collected in the PolyU-NIRFD database. The indoor hyperspectral face acquisition system was built which mainly consists of a CRI's VariSpec LCTF and a Halogen Light, and includes a hyperspectral dataset of hyperspectral image cubes from 25 volunteers with age range from 21 to 33 8 female and 17 male. For each individual, several sessions were collected with an average time space of 5 month. The minimal interval is 3 months and the maximum is 10 months. Each session consists of three hyperspectral cubes - frontal, right and left views with neutral-expression. The spectral range is from nm to nm with a step length of 10 nm, producing 33 bands in all. Since the database was constructed over a long period of time, significant appearance variations of the subjects, e. In data collection, positions of the camera, light and subject are fixed, which allows us to concentrate on the spectral characteristics for face recognition without masking from environmental changes. The database has a female-male ratio or nearly 1: This led to a diverse bi-modal database with both native and non-native English speakers. In total 12 sessions were captured for each client: The Phase I data consists of 21 questions with the question types ranging from: The Phase II data consists of 11 questions with the question types ranging from: The database was recorded using two mobile devices: The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone. The database is being made available by Dr. The images were acquired using a stereo imaging system at a high spatial resolution of 0. The color and range images were captured simultaneously and thus are perfectly registered to each other. All faces have been normalized to the frontal position and the tip of the nose is positioned at the center of the image. The images are of adult humans from all the major ethnic groups and both genders. These fiducial points were located manually on the facial color images using a computer based graphical user interface. Specific data partitions training, gallery, and probe that were employed at LIVE to develop the Anthropometric 3D Face Recognition algorithm are also available. The database contains both spontaneous and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database also includes expression images with and without glasses. FEI Face Database. There are 14 images for each of individuals, a total of images. All images are colourful and taken against a white homogenous background in an upright frontal position with profile rotation of up to about degrees. All faces are mainly represented by students and staff at FEI, between 19 and 40 years old with distinct appearance, hairstyle, and adorns. The number of male and female subjects are exactly the same and equal to An array of three cameras was placed above several portals natural choke points in terms of pedestrian traffic to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images ie. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal. The dataset consists of 25 subjects 19 male and 6 female in portal 1 and 29 subjects 23 male and 6 female in portal 2. In total, the dataset consists of 54 video sequences and 64, labelled face images. UMB database of 3D occluded faces. The database is available to universities and research centers interested in face detection, face recognition, face synthesis, etc. The main characteristics of VADANA, which distinguish it from current benchmarks, is the large number of intra-personal pairs order of thousand ; natural variations in pose, expression and illumination; and the rich set of additional meta-data provided along with standard partitions for direct comparison and bench-marking efforts. MORPH database is the largest publicly available longitudinal face database. The MORPH database contains 55, images of more than 13, people within the age ranges of 16 to There are an average of 4 images per individual with the time span between each image being an average of days. This data set was comprised for research on facial analytics and facial recognition. Face images of subjects 70 males and 30 females were captured; for each subject one image was captured at each distance in daytime and nighttime. All the images of individual subjects are frontal faces without glasses and collected in a single sitting. Face recognition using photometric stereo. This unique 3D face database is amongst the largest currently available, containing sessions of subjects, captured in two recording periods of approximately six months each. The Photoface device was located in an unsupervised corridor allowing real-world and unconstrained capture. Each session comprises four differently lit colour photographs of the subject, from which surface normal and albedo estimations can be calculated photometric stereo Matlab code implementation included. This allows for many testing scenarios and data fusion modalities. Eleven facial landmarks have been manually located on each session for alignment purposes. Additionally, the Photoface Query Tool is supplied implemented in Matlab , which allows for subsets of the database to be extracted according to selected metadata e. The Dataset consists of multimodal facial images of 52 people 14 females, 38 males acquired with a Kinect sensor. The data is captured in two sessions at different intervals of about two weeks. In each session, 9 facial images are collected from each person according to different facial expressions, lighting and occlusion conditions: An RGB color image, a depth map provided both as a bitmap depth image and a text file containing the original depth levels sensed by Kinect as well as the associated 3D data are provided for all samples. In addition, the dataset includes 6 manually labeled landmark positions for every face: YouTube Faces Database. The data set contains 3, videos of 1, different people. All the videos were downloaded from YouTube. An average of 2. The shortest clip duration is 48 frames, the longest clip is 6, frames, and the average length of a video clip is In designing our video data set and benchmarks we follow the example of the 'Labeled Faces in the Wild' LFW image collection. Specifically, our goal is to produce a large scale collection of videos along with labels indicating the identities of a person appearing in each video. In addition, we publish benchmark tests, intended to measure the performance of video pair-matching techniques on these videos. Finally, we provide descriptor encodings for the faces appearing in these videos, using well established descriptor methods. The dataset consists of subjects, specifically Caucasian females, from YouTube makeup tutorials. Images of the subjects before and after the application of makeup were captured. There are four shots per subject: For a few subjects, three shots each before and after the application of makeup were obtained. The program may exceed the authority granted to DHS by Congress because Congress has never explicitly authorized biometric collections from Americans at the border. Congress has passed legislation at least nine times concerning authorization for the collection of biometric data from foreign nationals, but no law directly authorizes DHS to collect the biometrics of Americans at the border. It never has. Without explicit authorization, DHS cannot and should not be scanning the faces of Americans as they depart on international flights, as it is currently doing. This is not the first time DHS has deployed a new privacy-invasive tool without conducting a required rulemaking process. In fact, a few years ago, under similar circumstances, a federal appeals court held that DHS was required to go through the rulemaking process before using body scanners at Transportation Security Administration TSA checkpoints. DHS must conduct a rulemaking because mandatory biometric screening, like the body scanners program, constitutes a policy with the force of law. Face scans are strictly mandatory for foreign nationals, and although DHS has said that face scans may be optional for some American citizens, it is unclear whether this is made known to American travelers. DHS has never measured the efficacy of airport face scans at catching impostors traveling with fraudulent credentials. Due to the challenges inherent to face recognition, it would be difficult for DHS to develop a system that is effective at catching every impostor without severely inconveniencing all other travelers. DHS currently measures performance based on how often the system correctly accepts travelers who are using true credentials. Yet DHS is not measuring that. As an analogy, consider a bouncer hired to check IDs at a bar. But the owner will almost certainly fire a bouncer who consistently allows entry to underage patrons using fake IDs. Like a bar owner who has not even asked how well a bouncer can identify fake IDs, DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. In fact, it may not be possible, given the current state of face recognition technology, to succeed on both of these fronts. There is an unavoidable trade-off between these two metrics: A system calibrated to reduce rejections of travelers using valid credentials will increase acceptance rates for impostors. Face recognition technology is not perfect. In reality, face recognition systems make mistakes on both of those fronts. A system may mistakenly reject a traveler flying under his own identity, for example, because his photo on file was taken four years prior and he has changed appearance since then. DHS clearly is focusing on making its face scan system minimally inconvenient for travelers using valid credentials. Indeed, analysis of face recognition algorithms indicates that some likely comparable systems would not perform very well at screening the type of impostor the system is likely to encounter: According to research conducted by the National Institute of Standards and Technology NIST , face recognition systems, like humans, have a harder time distinguishing among people who look alike. DHS indicated that it has been testing whether its face scanning system exhibits bias. Differential error rates could mean that innocent people will be pulled from the line at the boarding gate and subjected to manual fingerprinting at higher rates as a result of their complexion or gender. But because DHS has subsumed its evaluative process into a neutral-seeming computer algorithm, this bias may go undetected. Since February , NIST has tested more than 35 different face recognition algorithms designed to verify identities. Most face scanning algorithms function by first calculating the approximate similarity of two images presented for comparison, then accepting the presented images if the similarity calculation is greater than a predetermined match threshold, and rejecting the presented images if the calculation falls below the threshold. At the same time, the tested algorithms were more likely to mistakenly accept women, especially black women. Face recognition may perform differently as a result of variations in race or gender. The effects of these policies on free speech and association could be significant. DHS intends to subject every single traveler who departs for an international destination—American and foreign national alike—to biometric exit. Right now, scans generally take place at international departure gates and are conducted as travelers board the plane, arguably with the awareness of the scanned individual. But DHS is already exploring expansions to other areas of the airport. As DHS invests hundreds of millions of dollars into expanding its face scanning capability, airport face scans could even be extended to include passive scans throughout American airports—including of domestic travelers in domestic airports. The technology could also be adapted for purposes unrelated to air travel, including general law enforcement and counterterrorism initiatives. The broader reaching and more constant face scans become, the more they will threaten to chill free speech and thwart free association in airports. But this program also makes travelers vulnerable to increased and unconstrained tracking by private companies. At every step of the biometric exit process, private entities are heavily involved. Some airlines may well begin to explore ways to further monetize the technology they develop, for example by enhancing targeted advertising capabilities in airports. Some, if not all, of what airlines and technology partners may do with any data or technology to which they gain access through participation in biometric exit may be constrained by agreements with DHS. Without greater transparency regarding such private agreements and without substantive rules governing the role of private entities in the biometric exit process, there are few protections to ensure that biometric exit data and technology will not be abused. As it currently stands, the biometric exit program is unjustified. If the program is indeed designed to address visa overstay travel fraud, then DHS should study how often this type of fraud likely occurs, publish the results, and demonstrate that it is a problem worth solving. This could be done using data already available to the agency. For example, DHS could review historical data concerning the incidence of visa overstays entering U. DHS should suspend all airport face scans at departure gates until it comes into compliance with federal administrative law. As detailed above, the law requires DHS to solicit and consider comments from the public before adopting big-impact new programs like mandatory biometric scans. DHS must issue a Notice of Proposed Rulemaking, respond to public comments, and issue a Final Rule putting the public on notice about airport face scans and the rules that apply to them. DHS should exclude Americans from any biometric exit program. Congress has never explicitly authorized DHS to routinely scan the faces of U. A major issue hindering new developments in the area of Automatic Human Behaviour Analysis in general, and affect recognition in particular, is the lack of databases with displays of behaviour and affect. To address this problem, the MMI-Facial Expression database was conceived in as a resource for building and evaluating facial expression recognition algorithms..

Wechsler and Dr. The images were collected in a semi-controlled Fully public facial. To maintain a degree of consistency throughout the database, the same physical setup was used in each photography session. Because the equipment Fully public facial to be reassembled for each session, there was some minor Fully public facial in images collected on different dates.

The database contains sets of images for a total of 14, images that includes individuals and duplicate sets of images.

A duplicate set is a second set of images of a person already in the database and was usually taken on a different day. For some individuals, over two years had elapsed between their first visit web page last sittings, with some subjects being photographed multiple times.

This time lapse was important because it enabled researchers to study, for the first time, changes in a subject's appearance that occur over a year. SCface is a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains static images in visible and infrared spectrum of subjects. Images from different quality cameras mimic the real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios.

SCface database is freely available to research community. The paper describing the database is available here. SCfaceDB Landmarks. The database is comprised of 21 facial landmarks from face images from users annotated manually by a human operator, as described in this paper. A close relationship exists between the advancement of face recognition algorithms Fully public facial the availability of face databases varying factors that affect facial appearance in a controlled manner.

The Fully public facial database, collected at Carnegie Mellon University inhas been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: It contains subjects, captured Fully public facial 15 view points and 19 illumination conditions in four recording sessions for a total of more thanimages.

The Yale Face Database. Contains grayscale images in GIF format of 15 individuals. There Fully public facial 11 images per subject, one per different facial Fully public facial or configuration: The Yale Source Database B.

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Contains single light source images of 10 subjects each seen under viewing conditions 9 poses x 64 illumination conditions. For every subject in a particular pose, Fully public facial image with ambient background illumination was also captured.

A database of 41, images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions. Capturing scenario mimics the real world applications, for example, when a person is going through the airport check-in point. Six cameras capture human faces from three different read article. Three out of the six cameras have smaller focus length, and the other three have larger focus length.

Plan to capture subjects in 3 sessions in different time period. For one session, both in-door and out-door scenario will be captured. User-dependent pose and expression variation are expected from the video sequences. Fully public facial different images Fully public facial each of 40 distinct subjects.

guam naked Watch Video Xxx Sahibe. Somewhat ironically, it soon transpires that Moscow's surveillance playbook could be right out of Beijing. During the soccer World Cup last year, Russia famously deployed facial recognition to help police the event. All change in , though, when Moscow plans to catapult the city into the Chinese Super League for facial recognition. This year, we intend to increase the number to ,, meaning all the cameras installed in public areas to optimize the use of the system. The system captures 1. But it isn't just CCTV. Remember the stories put out by China's PR machine, of police officers equipped with AI smart-glasses? Well, Moscow plans the same. FindFace is a highly accurate facial recognition engine, the standout in a country known for its prowess in the space. But Lysenko also talks of international collaboration, with China being the obvious place to start. Of course, we are interested in exchanging experience with our foreign colleagues, including China, which has advanced in the deployment of the facial recognition system more than other countries. Which brings the discussion to 5G. The only constraint on these faces is that they were detected by the Viola-Jones face detector. Please see the database web page and the technical report linked there for more details. The LFWcrop Database. In the vast majority of images almost all of the background is omitted. LFWcrop was created due to concern about the misuse of the original LFW dataset, where face matching accuracy can be unrealistically boosted through the use of background parts of images i. As the location and size of faces in LFW was determined through the use of an automatic face locator detector , the cropped faces in LFWcrop exhibit real-life conditions, including mis-alignment, scale variations, in-plane as well as out-of-plane rotations. The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Wild data set, however, here we provide them after alignment using a commercial face alignment software. Some of our results were produced using these images. We show this alignment to improve the performance of face recognition algorithms. We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in the original image set. Note, however, that the images available here are grayscale versions of the originals. For each session, three shots were recorded with different but limited orientations of the head. Details about the population and typical problems affecting the quality are given in the referred link. The quality was limited but sufficient to show the ability of 3D face recognition. For privacy reasons, the texture images are not made available. In the period , this database has been downloaded by about researchers. A few papers present recognition results with the database like, of course, papers from the author. GavabDB is a 3D face database. It contains three-dimensional images of facial surfaces. These meshes correspond to 61 different individuals 45 male and 16 female having 9 images for each person. The total of the individuals are Caucasian and their age is between 18 and 40 years old. Each image is given by a mesh of connected 3D points of the facial surface without texture. The database provides systematic variations with respect to the pose and the facial expression. In particular, the 9 images corresponding to each individual are: This database is formed by up to subjects 75 men and 34 women , with 32 colour images per person. Each picture has a x pixel resolution, with the face occupying most of the image in an upright position. For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. In all cases, the background is plain and dark blue. The 32 images were classified in six groups according to the pose and lighting conditions: This database is delivered for free exclusively for research purposes. This database contains subjects, with approximately one woman every three men. If needed, the corresponding range data 2. Therefore, it is a multimodal database 2D, 2. During all time, a strict acquisition protocol was followed, with controlled lighting conditions. The person sat down on an adjustable stool opposite the scanner and in front of a blue wall. No glasses, hats or scarves were allowed. A total of 16 captures per person were taken in every session, with different poses and lighting conditions, trying to cover all possible variations, including turns in different directions, gestures and lighting changes. In every case only one parameter was modified between two captures. This is one of the main advantages of this database, respect to others. There are females and males in the database. Everyone has a 3D face data with neutral expression and without accessories. Original high-resolution 3D face data is acquired by the CyberWare 3D scanner in given environment, Every 3D face data has been preprocessed, and cut the redundant parts. Now the face database is available for research purpose only. The Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory in Beijing University of Technology is serving as the technical agent for distribution of the database and reserves the copyright of all the data in the database. The Bosphorus Database. The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from three aspects: Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. PUT Face Database. PUT Face Database consists of almost hi-res images of people. Images were taken in controlled conditions and the database is supplied with additional data including: Database is available for research purposes. The BFM consists of a generative 3D shape model covering the face surface from ear to ear and a high quality texture model. The model can be used either directly for 2D and 3D face recognition or to generate training and test images for any imaging condition. Hence, in addition to being a valuable model for face analysis it can also be viewed as a meta-database which allows the creation of accurately labeled synthetic training and testing images. The BFM web page additionally provides a set of registered scans of ten individuals, together with a set of renderings of these individuals with systematic pose and light variations. These scans are not included in the training set of the BFM and form a standardized test set with a ground truth for pose and illumination. Plastic Surgery Face Database. The plastic surgery face database is a real world database that contains pre and post surgery images pertaining to subjects. Different types of facial plastic surgeries have different impact on facial features. To enable the researchers to design and evaluate face recognition algorithms on all types of facial plastic surgeries, the database contains images from a wide variety of cases such as Rhinoplasty nose surgery , Blepharoplasty eyelid surgery , brow lift, skin peeling, and Rhytidectomy face lift. For each individual, there are two frontal face images with proper illumination and neutral expression: The database contains image pairs corresponding to local surgeries and cases of global surgery e. The details of the database and performance evaluation of several well known face recognition algorithms is available in this paper. IFDB is a large database that can support studies of the age classification systems. It contains over 3, color images. IFDB can be used for age classification, facial feature extraction, aging, facial ratio extraction, percent of facial similarity, facial surgery, race detection and other similar researches. The NIR face image acquisition system consists of a camera, an LED light source, a filter, a frame grabber card and a computer. The active light source is in the NIR spectrum between nm - 1, nm. The peak wavelength is nm. The strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera face distance is between 80 cm - cm, which is convenient for the users. By using the data acquisition device described above, we collected NIR face images from subjects. Then the subject was asked to make expression and pose changes and the corresponding images were collected. To collect face images with scale variations, we asked the subjects to move near to or away from the camera in a certain range. At last, to collect face images with time variations, samples from 15 subjects were collected at two different times with an interval of more than two months. In each recording, we collected about images from each subject, and in total about 34, images were collected in the PolyU-NIRFD database. The indoor hyperspectral face acquisition system was built which mainly consists of a CRI's VariSpec LCTF and a Halogen Light, and includes a hyperspectral dataset of hyperspectral image cubes from 25 volunteers with age range from 21 to 33 8 female and 17 male. For each individual, several sessions were collected with an average time space of 5 month. The minimal interval is 3 months and the maximum is 10 months. Each session consists of three hyperspectral cubes - frontal, right and left views with neutral-expression. The spectral range is from nm to nm with a step length of 10 nm, producing 33 bands in all. Since the database was constructed over a long period of time, significant appearance variations of the subjects, e. In data collection, positions of the camera, light and subject are fixed, which allows us to concentrate on the spectral characteristics for face recognition without masking from environmental changes. The database has a female-male ratio or nearly 1: This led to a diverse bi-modal database with both native and non-native English speakers. In total 12 sessions were captured for each client: The Phase I data consists of 21 questions with the question types ranging from: The Phase II data consists of 11 questions with the question types ranging from: The database was recorded using two mobile devices: The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone. The database is being made available by Dr. The images were acquired using a stereo imaging system at a high spatial resolution of 0. The color and range images were captured simultaneously and thus are perfectly registered to each other. All faces have been normalized to the frontal position and the tip of the nose is positioned at the center of the image. The images are of adult humans from all the major ethnic groups and both genders. These fiducial points were located manually on the facial color images using a computer based graphical user interface. Specific data partitions training, gallery, and probe that were employed at LIVE to develop the Anthropometric 3D Face Recognition algorithm are also available. The database contains both spontaneous and posed expressions of more than subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database also includes expression images with and without glasses. FEI Face Database. There are 14 images for each of individuals, a total of images. All images are colourful and taken against a white homogenous background in an upright frontal position with profile rotation of up to about degrees. All faces are mainly represented by students and staff at FEI, between 19 and 40 years old with distinct appearance, hairstyle, and adorns. The number of male and female subjects are exactly the same and equal to An array of three cameras was placed above several portals natural choke points in terms of pedestrian traffic to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images ie. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal. The dataset consists of 25 subjects 19 male and 6 female in portal 1 and 29 subjects 23 male and 6 female in portal 2. In total, the dataset consists of 54 video sequences and 64, labelled face images. UMB database of 3D occluded faces. The database is available to universities and research centers interested in face detection, face recognition, face synthesis, etc. The main characteristics of VADANA, which distinguish it from current benchmarks, is the large number of intra-personal pairs order of thousand ; natural variations in pose, expression and illumination; and the rich set of additional meta-data provided along with standard partitions for direct comparison and bench-marking efforts. MORPH database is the largest publicly available longitudinal face database. The MORPH database contains 55, images of more than 13, people within the age ranges of 16 to There are an average of 4 images per individual with the time span between each image being an average of days. This data set was comprised for research on facial analytics and facial recognition. Face images of subjects 70 males and 30 females were captured; for each subject one image was captured at each distance in daytime and nighttime. All the images of individual subjects are frontal faces without glasses and collected in a single sitting. Face recognition using photometric stereo. This unique 3D face database is amongst the largest currently available, containing sessions of subjects, captured in two recording periods of approximately six months each. The Photoface device was located in an unsupervised corridor allowing real-world and unconstrained capture. At a rate of 1 in 25, that would mean passengers would be wrongfully denied boarding at Logan Airport on a daily basis. See infra Section C. See infra Section D. DHS should justify its investment in face scans by supplying evidence of the problem it purportedly solves. DHS should stop scanning the faces of American citizens as they leave the country. DHS should prove that airport face scans are capable of identifying impostors without inconveniencing everyone else. DHS should adopt a public policy that prohibits secondary uses of the data collected by its airport face scan program. DHS should provide fairness and privacy guarantees to the airlines with which it partners. Figure 2: A traveler has his face scanned as a Customs and Border Protection agent provides instruction. Associated Press, all rights reserved. Sidebar 1: What Is Biometric Exit? Partner Process 4 June 12, , https: Because accuracy is highly dependent on image quality, the inclusion of photos from sources other than passport and visa databases, such as law enforcement encounters, likely lowers overall system accuracy rates beyond what is assumed in this paper. Partner Process, supra note 4, at 3—4. See id. Regulatory Impact Analysis 67—68 Apr. Homeland Security officials say they believe the entry and exit biometric system can also be used to crack down on illegal immigration. In the absence of a biometric entry and exit system, the agency depends on incomplete data from airline passenger manifests to track people who leave the country. Sidebar 2: National Commission on Terrorist Attacks upon the U. This includes foreign nationals except those who are under the age of 14, over the age of 79, and diplomats. See Ron Nixon, supra note See supra note 9, at 8. We think it gives us immigration and counterterrorism benefits. See supra note When courts review the text of a law to determine congressional intent, courts will often apply a canon of statutory construction known as expressio unius est exclusio alterius , or more plainly, the expression-exclusion rule. Vonn, U. See also Chevron U. Echazabal, U. Crawford, Construction of Statutes ; Ford v. Under this canon of statutory construction, courts would likely read the aforementioned nine laws and conclude that Congress did not authorize face scans of Americans exiting the country. Examining the Problem of Visa Overstays: A Need for Better Tracking and Accountability: Customs and Border Protection; and Louis A. Immigration and Customs Enforcement as of June , https: Department of Homeland Security, F. See, e. Customs and Border Protection Aug. Customs and Border Protection, July 11, , https: See 73 Fed. For some participating airlines, for instance, a traveler may request not to participate in the TVS and instead present credentials to airline personnel. Partner Process, supra note 4, at However, in a more recent public meeting, in response to a question about testing for accuracy, a DHS spokesperson acknowledged it cannot measure impostor rates, stating: DHS appears to have no idea whether its system will be effective at achieving its primary technical objective. At a False Reject rate of 1 in 1, travelers, the 38 most recent algorithms studied produced an average False Accept rate of 9. Lowering the rate of false rejects to 1 in , travelers raised the average rate of False Accepts to more than 27 percent. False Accept rates configured in typical operational systems. This is also intuitive. A face recognition system that allows everyone to board will have no false rejections, but it will allow percent of impostors through the gate. In reverse, a system that rejects everyone would never permit an impostor to board but would have a False Reject rate of percent. The MMI Facial Expression Database is an ongoing project, that aims to deliver large volumes of visual data of facial expressions to the facial expression analysis community. A major issue hindering new developments in the area of Automatic Human Behaviour Analysis in general, and affect recognition in particular, is the lack of databases with displays of behaviour and affect..

All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position with tolerance for some side movement. They ranged go here age from 18 to 30 years. Sixty-five percent were female, 15 percent were African-American, and three percent were Asian or Latino. Subjects were instructed by an experimenter to perform a series of 23 facial displays that included single action units and combinations of Fully public facial units.

Image sequences from neutral to target display were digitized into by or pixel arrays with 8-bit precision for grayscale values. Included with the image files are "sequence" files; these are short text files that describe the order in which images should be read. It provides two training sets: High resolution pictures, including frontal, half-profile and profile view; 2. The head models were generated by fitting a morphable model to the high-resolution training images.

The 3D models are not included in the database. The test set consists of images per subject. We varied the illumination, pose up to about read article degrees of rotation in depth and the background.

Read article, about 7, color images are included in the database, and each has a matching gray Fully public facial image used in the neural network analysis.

Contains images of people of various racial origins, mainly of first year undergraduate students, so the majority of indivuals are between years old but some older individuals are also present. Some individuals are wearing glasses and beards.

There are images of individuals cases male and 78 female. The database contains both front and side profile views when available. Separating front views and profiles, there are cases with two or more front views and with only Fully public facial front view. Profiles have 89 cases with two or more profiles and with only one profile. Cases with both fronts and profiles have click at this page cases with two or more of both fronts Fully public facial profiles, 27 with two or more fronts and one profile, and with only one front and one profile.

JPEG format. Database is made up from 37 different faces and provides 5 shots for each person. These shots were taken at one week intervals Fully public facial when drastic face changes occurred in the meantime. Also, they have been asked to rotate the head once again without glasses if they wear any. Contains four recordings of subjects taken over a period of four months. Each recording contains a speaking head shot and a rotating head shot. Sets of Fully public facial taken from this database are available including high quality colour images, 32 KHz bit sound files, video sequences and a 3D model.

Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions sun glasses and scarf. Contains different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Faces in frontal position captured under Horizon, Incandescent, Fluorescent and Daylight illuminant.

Includes 3 spectral reflectance of skin per person measured from both cheeks and forehead. Contains RGB spectral response of camera used and spectral power distribution of illuminants. The goals to create the PEAL face database include: Each image has been rated on 6 emotion adjectives by 60 Japanese subjects. The dataset consists of gray level images with a resolution of x pixel. Each one shows the frontal view of a face of one out of 23 different Fully public facial persons.

For comparison reasons the set also contains manually set eye postions. Fully public facial is a collection Fully public facial images useful for research in Psychology, such as sets of faces and objects. The images in the database are organised into SETS, with each set often representing a separate experimental study. The Sheffield Face Database previously: Consists Fully public facial images of 20 people.

Each covering a range of poses from profile to frontal views. Each subject exists in their own directory labelled 1a, 1b, The files are all in PGM format, approximately x pixels in shades of grey. This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in at the Max Planck Institute for Biological Cybernetics. The Fully public facial cameras were arranged at 18 degrees intervals in a semi-circle around the subject at a distance of roughly 1.

In order to facilitate the recovery of rigid head motion, the subject wore a headplate with 6 green markers. The website contains a total of Fully public facial sequences in MPEG1 format. Caltech Faces. Human identification from facial features Fully public facial been studied primarily using imagery from visible video cameras. Big Tit Girl Fucked Hard.

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