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.