R. Chellappa, P. Sinha, and P. J. Phillips, Face Recognition by Computers and Humans, Computer, vol.43, issue.2, pp.46-55, 2010.
DOI : 10.1109/MC.2010.37

H. Wechsler, Reliable face recognition methods: system design, implemen- 887 tation and evaluation, p.888, 2009.
DOI : 10.1007/978-0-387-38464-1

H. Guo, W. R. Schwartz, and L. S. Davis, Face verification using large feature 889 sets and one shot similarity, International Joint 890 Conference on, pp.1-8, 2011.
DOI : 10.1109/ijcb.2011.6117498

W. R. Schwartz, H. Guo, J. Choi, and L. S. Davis, Face identification using 892 large feature sets, Image Processing, IEEE Transactions on, vol.21, issue.893, pp.2245-2255, 2012.
DOI : 10.1109/tip.2011.2176951
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.575

G. C. Paulo, H. Pedrini, W. R. Schwartz, A. Joly, C. Frélicot et al., Classification schemes based on Feature statistical retrieval applied to 976 content based copy identification, Image Processing (ICIP). IEEE In- 977 ternational Conference on, pp.681-684, 2004.

]. S. Poullot, O. Buisson, and M. Crucianu, Z-grid-based probabilistic retrieval for 979 scaling up content-based copy detection, in: Image and Video Retrieval, 6th 980 ACM International Conference on, pp.978-348, 2007.

]. B. Kulis, P. Jain, and K. Grauman, Fast Similarity Search for Learned Metrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, pp.981-982
DOI : 10.1109/TPAMI.2009.151

]. R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for 988 collaborative filtering, Machine learning. 24th International Conference 989 on, pp.987-791, 2007.

]. A. Joly and O. Buisson, Random maximum margin hashing, CVPR 2011, pp.990-992, 2011.
DOI : 10.1109/CVPR.2011.5995709
URL : https://hal.archives-ouvertes.fr/hal-00642178

]. A. Torralba, R. Fergus, and Y. Weiss, Small codes and large image databases 994 for recognition, Computer Vision and Pattern Recognition (CVPR), p.993
DOI : 10.1109/cvpr.2008.4587633
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.6991

]. P. Indyk and R. Motwani, Approximate nearest neighbors, Proceedings of the thirtieth annual ACM symposium on Theory of computing , STOC '98, pp.1002-604, 1998.
DOI : 10.1145/276698.276876

]. R. Rosipal and N. Krämer, Overview and recent advances in partial least 1006 squares, in: Subspace, latent structure and feature selection, pp.1005-1039, 1007.

W. R. Srinivasan, R. Schwartz, L. Duraiswami, and . Davis, Partial 1011 least squares on graphical processor for efficient pattern recognition, p.1013, 1010.

]. T. Mehmood, K. H. Liland, L. Snipen, and S. Saebø, A review of variable se- 1015 lection methods in partial least squares regression, Chemometrics and In- 1016 telligent, Laboratory Systems, vol.118, pp.1014-62, 2012.

P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, The FERET evaluation 1018 methodology for face-recognition algorithms, Pattern Analysis and Machine, Intelligence, vol.22, issue.10, pp.1017-1090, 1019.

]. H. Wang, S. Z. Li, and Y. Wang, Face recognition under varying lighting con- 1021 ditions using self quotient image, in: Automatic Face and Gesture Recog- 1022 nition, pp.1020-819, 2004.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang et al., Overview of the face recognition grand 1025 challenge, Computer vision and pattern recognition (CVPR). IEEE 1026 Conference on, pp.1023-947, 1024.

]. T. Ahonen, A. Hadid, and M. Pietikainen, Face description with local binary 1028 patterns: Application to face recognition, Pattern Analysis and Machine 1029, p.1027

]. T. Randen and J. H. Husoy, Filtering for texture classification: A comparative 1031 study, Pattern Analysis and Machine Intelligence Histograms of oriented gradients for human detection, Computer Vision and Pattern Recognition (CVPR). IEEE Computer, p.1030, 1033.

S. D. Jong, Simpls: an alternative approach to partial least squares re- 1036 gression, Chemometrics and intelligent laboratory systems, pp.1035-251, 1993.

G. Zeng and . Li, Incremental partial least squares analysis of big 1039 streaming data, Pattern Recognition, vol.47, issue.11, pp.1038-3726, 2014.