Face recognition vendor test 2002

By P. J. Phillips, Patrick Grother, R. Micheals, D. Blackburn, Elham Tabassi, Mike Bone. 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443), 2003

421 citations recorded by the source corpus.

Summary form only given. The face recognition vendor test (FRVT) 2002 is an independently administered technology evaluation of mature face recognition systems. FRVT 2002 provides performance measures for assessing the capability of face recognition systems to meet requirements for large-scale, real-world applications. Participation in FRVT 2002 was open to commercial and mature prototype systems from universities, research institutes, and companies. Ten companies submitted either commercial or prototype systems. FRVT 2002 computed performance statistics on an extremely large data set-121,589 operational facial images of 37,437 individuals. FRVT 2002 1) characterized identification and watch list performance as a function of database size, 2) estimated the variability in performance for different groups of people, 3) characterized performance as a function of elapsed time between enrolled and new images of a person, and 4) investigated the effect of demographics on performance. FRVT 2002 showed that recognition from indoor images has made substantial progress since FRVT 2000. Demographic results show that males are easier to recognize than females and that older people are easier to recognize than younger people. FRVT 2002 also assessed the impact of three new techniques for improving face recognition: three-dimensional morphable models, normalization of similarity scores, and face recognition from video sequences. Results show that three-dimensional morphable models and normalization increase performance and that face recognition from video sequences offers only a limited increase in performance over still images. A new XML-based evaluation protocol was developed for FRVT 2002. This protocol is flexible and supports evaluations of biometrics in general The FRVT 2002 reports can be found at http://www.frvt.org.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/491472dcde6783f5607f69b7922fb4dcc21f4bcb

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Citations in the corpus

Open set face recognition using transduction (2005, methodology): “, previously enrolled) to a system, the open set identification rate, is used to quantify user [hit] performance” [7].”
A Survey of Face Recognition Techniques (2009, background): “Evaluations of state-of-the-art recognition techniques conducted during the past several years, such as the FERET evaluations [7, 38], FRVT 2000 [39], FRVT 2002 [40] and the FAT 2004 [41], have confirmed that age variations, illumination variations and pose variations are three major problems…”
Recognizing Rotated Faces From Frontal and Side Views: An Approach Toward Effective Use of Mugshot Databases (2008, neutral): “nition and computer vision [42], [62].”
3D Morphable Face Models—Past, Present, and Future (2019, positive): “Ms as a tool for face frontalization [Blanz et al. 2005; Hassner et al. 2015; Tena et al. 2007]. In this context, it was shown that 9 out of 10 2D algorithms in the Face Recognition Vendor Test 2002 [Phillips et al. 2003] improved considerably when combined with a 3DMM for face frontalization [Blanz”
Towards causal benchmarking of bias in face analysis algorithms (2020, background): “Benchmarking in computer vision has a long history [6, 9, 22] including face recognition [47, 60, 61, 62, 27, 28] and face analysis [12].”
Characterizing the Variability in Face Recognition Accuracy Relative to Race (2019, background): “Observations of face recognition accuracy varying across demographic groups date at least to the 2002 Face Recognition Vendor Test (FRVT) [5].”
Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network (2018, background): “Amazon’s Rekognition Tool incorrectly matched the photos of 28 U.S. congressmen with the faces of criminals, especially the error rate was up to 39% for non-Caucasian people. Although several studies [49, 29, 23, 50, 36] have uncovered racial bias in non-deep FR algorithms, this ﬁeld still remains t”