Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition
Where Published: Face Recognition: From Theories to Applications, H. Wechsler, V. Bruce, T. Huang, J. P. Phillips, eds., Springer-Verlag, Berlin, 1998.
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 ARPA's 1996 FERET face recognition competition, in which this algorithm was found to be the top performer.
