Beyond eigenfaces: probabilistic matching for face recognition Export

@inproceedings{citeulike:3149844, abstract = {We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image differences. Specifically we model lure mutually exclusive classes of variation between facial images: intra-personal (variations in appearance of the same individual, due to different expressions or lighting) and extra-personal (variations in appearance due to a difference in identity). The likelihoods for each respective class are learned from training data using eigenspace density estimation and used to compute similarity based on the a posteriori probability of membership in the intra-personal class, and ultimately used to rank matches in the database. The performance advantage of this probabilistic technique over nearest-neighbor eigenface matching is demonstrated using results front ARPA's 1996 \“FERET\” face recognition competition, in which this algorithm was found to be the top performer}, author = {Moghaddam, B. and Wahid, W. and Pentland, A.}, booktitle = {Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on}, citeulike-article-id = {3149844}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/AFGR.1998.670921}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=670921}, doi = {10.1109/AFGR.1998.670921}, journal = {Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on}, keywords = {demo, eigenfaces}, pages = {30--35}, posted-at = {2008-08-22 19:56:16}, priority = {2}, title = {Beyond eigenfaces: probabilistic matching for face recognition}, url = {http://dx.doi.org/10.1109/AFGR.1998.670921}, year = {1998} }

TY - CONF ID - citeulike:3149844 L3 - citeulike-article-id:3149844 N2 - We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image differences. Specifically we model lure mutually exclusive classes of variation between facial images: intra-personal (variations in appearance of the same individual, due to different expressions or lighting) and extra-personal (variations in appearance due to a difference in identity). The likelihoods for each respective class are learned from training data using eigenspace density estimation and used to compute similarity based on the a posteriori probability of membership in the intra-personal class, and ultimately used to rank matches in the database. The performance advantage of this probabilistic technique over nearest-neighbor eigenface matching is demonstrated using results front ARPA's 1996 “FERET” face recognition competition, in which this algorithm was found to be the top performer JF - Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on EP - 35 TI - Beyond eigenfaces: probabilistic matching for face recognition T2 - Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on SP - 30 KW - demo KW - eigenfaces AU - Moghaddam, B AU - Wahid, W AU - Pentland, A PY - 1998/// UR - http://dx.doi.org/10.1109/AFGR.1998.670921 ER -