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<div>NIST has published NISTIR 8331 - Ongoing FRVT Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms on November 30, 2020, the second out of a series of reports aimed at quantifying face recognition accuracy for people wearing masks. This report adds 1) 65 new algorithms submitted to FRVT 1:1 since mid-March 2020 (and includes cumulative results for 152 algorithms evaluated to date) and 2) assessment of when both the enrollment and verification images are masked (in addition to when only the verification image is masked). Our initial approach has been to apply masks to faces digitally (i.e., using software to apply a synthetic mask). This allowed us to leverage large datasets that we already have. This report quantifies the effect of masks on both false negative and false positives match rates. For more information, visit the FRVT Face Mask Effects webpage.</div><div></div><div></div><div></div><div></div><div></div><div>face recognition app download</div><div></div><div>DOWNLOAD: https://t.co/fIDfahoNI7 </div><div></div><div></div><div>NIST describes and quantifies demographic differentials for contemporary face recognition algorithms in this report, NISTIR 8280. NIST has conducted tests to quantify demographic differences for nearly 200 face recognition algorithms from nearly 100 developers, using four collections of photographs with more than 18 million images of more than 8 million people.</div><div></div><div></div><div>The FRVT Ongoing activity is conducted on a continuing basis and will remain open indefinitely such that developers may submit their algorithms to NIST whenever they are ready. This approach more closely aligns evaluation with development schedules. The evaluation will use very large sets of facial imagery to measure the performance of face recognition algorithms developed in commercial and academic communities worldwide. Multiple evaluation tracks relevant to face recognition will be conducted under this test. For more information, visit the FRVT Ongoing webpage.</div><div></div><div></div><div>The FRVT 1:N 2018 will measure advancements in the accuracy and speed of one-to-many face recognition identification algorithms searching enrolled galleries containing at least 10 million identities. The evaluation will primarily use standardized portrait images, and will quantify how accuracy depends on subject-specific demographics and image-specific quality factors. For more information, visit the FRVT 1:N 2018 webpage.</div><div></div><div></div><div></div><div></div><div></div><div></div><div>Facial morphing and the ability to detect it is an area of high interest to a number of photo-credential issuance agencies and those employing face recognition for identity verification. The FRVT MORPH test will provide ongoing independent testing of prototype facial morph detection technologies.</div><div></div><div></div><div>NIST is establishing an evaluation of face image quality assessment algorithms. NIST will run quality assessment algorithms on large sets of images and relate their outputs to face recognition outcomes.</div><div></div><div></div><div>While not part of the FRVT series, the Face-in-Video-Evaluation (FIVE) conducted 2015-2016 will be of interest to the FRVT audience. The FIVE activity assessed face recognition capability in video sequences. The outcomes of FIVE were published in NIST Interagency Report 8173.</div><div></div><div></div><div>The Face Recognition Algorithm Independent Evaluation (CHEXIA-FACE) was conducted to assess the capability of face detection and recognition algorithms to correctly detect and recognize children's faces appearing in unconstrained imagery.</div><div></div><div></div><div>FRVT 2013 tested state-of-the-art face recognition performance. It used very large sets of facial imagery to measure the accuracy and computational efficiency of face recognition algorithms developed in commercial and academic communities worldwide. The test itself ran from July 2012 to the end of 2013. The detailed plans, procedures and outcomes of the test are documented on the FRVT 2013 homepage.</div><div></div><div></div><div>Under the name MBE 2010, 2D face recognition algorithms were evaluated, yielding two reports. First, NIST Interagency Report 7709 gave results for both verification and identification algorithms. Second, the NIST Interagency Report 7830 surveyed compression and resolution parameters for storing face images on identity credentials.</div><div></div><div></div><div>FRVT 2000 consisted of two components: the Recognition Performance Test and the Product Usability Test. The Recognition Performance Test was a technology evaluation. The goal of the Recognition Performance Test was to compare competing techniques for performing facial recognition. All systems were tested on a standardized database. The standard database ensured all systems were evaluated using the same images, which allowed for comparison of the core face recognition technology. The product usability test examined system properties for performing access control.</div><div></div><div></div><div>The goal of the FERET program was to develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties. The task of the sponsored research was to develop face recognition algorithms. The FERET database was collected to support the sponsored research and the FERET evaluations. The FERET evaluations were performed to measure progress in algorithm development and identify future research directions.</div><div></div><div></div><div>Rite Aid will be prohibited from using facial recognition technology for surveillance purposes for five years to settle Federal Trade Commission charges that the retailer failed to implement reasonable procedures and prevent harm to consumers in its use of facial recognition technology in hundreds of stores.</div><div></div><div></div><div>In a complaint filed in federal court, the FTC says that from 2012 to 2020, Rite Aid deployed artificial intelligence-based facial recognition technology in order to identify customers who may have been engaged in shoplifting or other problematic behavior. The complaint, however, charges that the company failed to take reasonable measures to prevent harm to consumers, who, as a result, were erroneously accused by employees of wrongdoing because facial recognition technology falsely flagged the consumers as matching someone who had previously been identified as a shoplifter or other troublemaker.</div><div></div><div></div><div>The false matches were disproportionately of people of color, including six members of the Congressional Black Caucus, among them civil rights legend Rep. John Lewis (D-Ga.). These results demonstrate why Congress should join the ACLU in calling for a moratorium on law enforcement use of face surveillance.</div><div></div><div></div><div>Using Rekognition, we built a face database and search tool using 25,000 publicly available arrest photos. Then we searched that database against public photos of every current member of the House and Senate. We used the default match settings that Amazon sets for Rekognition.</div><div></div><div></div><div>These dangers are why Amazon employees, shareholders, a coalition of nearly 70 civil rights groups, over 400 members of the academic community, and more than 150,000 members of the public have already spoken up to demand that Amazon stop providing face surveillance to the government.</div><div></div><div></div><div>Find faces in aphotographFind faces in a photograph (using deeplearning)Find faces in batches of images w/ GPU (using deeplearning)Facial FeaturesIdentify specific facial features in aphotographApply (horribly ugly) digitalmake-upFacial RecognitionFind and recognize unknown faces in a photograph based onphotographs of knownpeopleCompare faces by numeric face distance instead of only True/FalsematchesRecognize faces in live video using your webcam - Simple / SlowerVersion (Requires OpenCV to beinstalled)Recognize faces in live video using your webcam - Faster Version(Requires OpenCV to beinstalled)Recognize faces in a video file and write out new video file(Requires OpenCV to beinstalled)Recognize faces on a Raspberry Pi w/cameraRun a web service to recognize faces via HTTP (Requires Flask to beinstalled)Recognize faces with a K-nearest neighborsclassifier</div><div></div><div></div><div>DOJ developed a privacy impact assessment (PIA) of NGI-IPS in 2008, as required under the E-Government Act whenever agencies develop technologies that collect personal information. However, the FBI did not update the NGI-IPS PIA in a timely manner when the system underwent significant changes or publish a PIA for FACE Services before that unit began supporting FBI agents. DOJ ultimately approved PIAs for NGI-IPS and FACE Services in September and May 2015, respectively. The timely publishing of PIAs would provide the public with greater assurance that the FBI is evaluating risks to privacy when implementing systems. Similarly, NGI-IPS has been in place since 2011, but DOJ did not publish a System of Records Notice (SORN) that addresses the FBI's use of face recognition capabilities, as required by law, until May 5, 2016, after completion of GAO's review. The timely publishing of a SORN would improve the public's understanding of how NGI uses and protects personal information.</div><div></div><div></div><div>Prior to deploying NGI-IPS, the FBI conducted limited testing to evaluate whether face recognition searches returned matches to persons in the database (the detection rate) within a candidate list of 50, but has not assessed how often errors occur. FBI officials stated that they do not know, and have not tested, the detection rate for candidate list sizes smaller than 50, which users sometimes request from the FBI. By conducting tests to verify that NGI-IPS is accurate for all allowable candidate list sizes, the FBI would have more reasonable assurance that NGI-IPS provides leads that help enhance, rather than hinder, criminal investigations. Additionally, the FBI has not taken steps to determine whether the face recognition systems used by external partners, such as states and federal agencies, are sufficiently accurate for use by FACE Services to support FBI investigations. By taking such steps, the FBI could better ensure the data received from external partners is sufficiently accurate and do not unnecessarily include photos of innocent people as investigative leads.</div><div></div><div></div><div>Technology advancements have increased the overall accuracy of automated face recognition over the past few decades. According to the FBI, this technology can help law enforcement agencies identify criminals in their investigations.</div><div></div><div> df19127ead</div>
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