Bayesian face recognition

We propose a new technique for direct visual matching of images for the pur poses of face recognition and image retrieval, using a probabilistic measur e of similarity, based primarily on a Bayesian (MAP) analysis of image diff erences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrat ed using results from DARPA's 1996 "FERET" face recognition competition, in which this Bayesian matching alogrithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of n onlinear (on-line) Bayesian similarity measures by inexpensive linear (off- line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large datab ases. (C) 2000 Pattern Recognition Society. Published by Elsevier Science L td. All rights reserved.