The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Overview of the Face Recognition Grand Challenge
Abstract
Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
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Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Research works printed but not linked: 6
- grother, 2004, Face recognition vendor test 2002: Supplemental report, NIST Technical Report, 7083
- 10.1109/TPAMI.2005.70 · DOI · Paper page
- phillips, 2003, Face recognition vendor test 2002: Evaluation report, NIST Technical Report, 6965
- 10.1088/0954-898X/5/4/006 · DOI · Paper page
- 10.1007/s00138-004-0144-7 · DOI
- 10.1068/p2896 · DOI · Paper page
Cited by
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“Many areas of computer vision, such as stereo [63], face recognition [28, 55, 68], and object recognition [23, 24], have challenging datasets to track the progress made by leading algorithms and to stimulate new ideas.”
BackgroundInformation revelation and privacy in online social networks
Gross, R., & Acquisti, A. (2005) · Proceedings of the 2005 ACM workshop on Privacy in the electronic society · doi:10.1145/1102199.1102214 · Cited by 2,028
Face recognition in unconstrained videos with matched background similarity
Wolf, L., Hassner, T., & Maoz, I. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995566 · Cited by 1,382
A 3D Facial Expression Database For Facial Behavior Research
Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.6 · Cited by 1,171
Bosphorus Database for 3D Face Analysis
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., & Akarun, L. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-89991-4_6 · Cited by 812
Metadata
- Authors
- Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 1
- Pages
- 947-954
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2137659841
- Cited by
- 2,438
- References
- 1
- FWCI
- 102.90
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005). Overview of the Face Recognition Grand Challenge. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). https://doi.org/10.1109/cvpr.2005.268
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