The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
FRVT 2006 and ICE 2006 Large-Scale Experimental Results
Abstract
This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.
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Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Face recognition: A literature survey
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,163
Self-citationThe FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Self-citationFace recognition by elastic bunch graph matching
Wiskott, L., Fellous, J.-M., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,883
Overview of the Face Recognition Grand Challenge
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.268 · Cited by 2,438
Self-citationThe FERET database and evaluation procedure for face-recognition algorithms
Phillips, P. J., Wechsler, H., Huang, J., & Rauss, P. J. (1998) · Image and Vision Computing · doi:10.1016/s0262-8856(97)00070-x · Cited by 2,431
Self-citationFace Recognition by Elastic Bunch Graph Matching*†
Wiskott, L., Fellous, J.-M., Krüger, N., & von der Malsburg, C. (2022) · INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION · doi:10.1201/9780203750520-11 · Cited by 1,781
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Bowyer, K. W., Chang, K., & Flynn, P. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.05.005 · Cited by 979
Self-citationImage understanding for iris biometrics: A survey
Bowyer, K. W., Hollingsworth, K., & Flynn, P. J. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.08.005 · Cited by 907
Self-citationComputational and Performance Aspects of PCA-Based Face-Recognition Algorithms
Moon, H., & Phillips, P. J. (2001) · Perception · doi:10.1068/p2896 · Cited by 509
Self-citation
Cited by
Face Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516
“The study in [17] was motivated by a rather surprising result in the 2006 NIST Face Recognition Vendor Test (FRVT) [19].”
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J. G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721355115 · Cited by 461
Self-citationIris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
Nguyen, K., Fookes, C., Ross, A., & Sridharan, S. (2018) · IEEE Access · doi:10.1109/access.2017.2784352 · Cited by 355
Learning Race from Face: A Survey
Fu, S., He, H., & Hou, Z.-G. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321570 · Cited by 178
CCTV on trial: Matching video images with the defendant in the dock
Davis, J. P., & Valentine, T. (2009) · Applied Cognitive Psychology · doi:10.1002/acp.1490 · Cited by 112
Metadata
- Authors
- Phillips, P. J., Scruggs, W. T., O'Toole, A. J., Flynn, P. J., Bowyer, K. W., Schott, C. L., & Sharpe, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-05
- Volume
- 32
- Issue
- 5
- Pages
- 831-846
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2133295669
- Cited by
- 406
- References
- 23
- FWCI
- 46.54
- Type
- journal article
- Language
- en
- Open access
- closed
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