Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition

We propose a novel technique for direct visual matching of images for the purposes of face recognition and database search. Specifically, we argue in favor of a probabilistic measure of similarity, in contrast to simpler methods which are based on standard Euclidean L2 norms (template matching) or subspace-restricted norms (eigenspace matching). The proposed similarity measure is based on a Bayesian analysis of image differences: we model two mutually exclusive classes of variation between two facial images: intra-personal (variations in appearance of the same individual, due to different expressions or lighting) and extra-personal (variations in appearance due to a difference in identity). The high-dimensional probability density functions for each respective class are then obtained from training data using an eigenspace density estimation technique and subsequently used to compute a similarity measure based on the a posteriori probability of membership in the intra-personal class, which is used to rank matches in the database. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenspace matching is demonstrated using results from ARPAś 1996 FERET face recognition competition, in which this algorithm was found to be the top performer. Face Recognition: From Theories to Applications, H. Wechsler, V. Bruce, T. Huang, J. P. Phillips, eds., Springer-Verlag, Berlin, 1998. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 1998 201 Broadway, Cambridge, Massachusetts 02139