Bayesian Modeling of Facial

In previous work 6, 9, 10], we 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 diier-ences, leading to a \dual" basis similar to eigenfaces 13]. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was recently demonstrated using results from DARPA's 1996 \FERET" face recognition competition, in which this probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the costly compution of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (ooine) subspace projections and simple (online) Euclidean norms, thus resulting in a signiicant computational speed-up for implementation with very large image databases as typically encountered in real-world applications.