Face Recognition Using Kernel Uncorrelated Discriminant Analysis

Licheng Jiao, Rui Hu, Weida Zhou, Yi Gao

Abstract: Feature extraction is one of the most important problems in face recognition task. In this paper, we use kernel uncorrelated discriminant analysis to extract the optimal discriminant features for face recognition. The method also solves the so-called “Small Sample Size” (SSS) problem, which exists in most Face Recognition tasks. Experimental results on the Yale face database and AT&T face database show the effectiveness of this method.