Abstract:

We propose 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 on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching is demonstrated using results from DARPA\'s 1996 \"FERET\" face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (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 databases.