Efficient MAP/ML similarity matching for visual recognition

Moghaddam et al. previously (1996, 1998) advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenspace matching was recently demonstrated using results from DARPA´s 1996 “FERET” face recognition competition, in which our probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the rather costly computation of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large image databases