Face authentication for multiple subjects using eigen#ow

In this paper, we present a novel scheme for face authentication. To deal with variations, such as facial expressions and registration errors, with which traditional intensity-based methods do not perform well, we propose the eigen#ow approach. In this approach, the optical #ow and the optical #ow residue between a test image and an image in the training set are 2rst computed. The optical #ow is then 2tted to a model that is pre-trained by applying principal component analysis to optical #ows resulting from facial expressions and registration errors for the subject. The eigen#ow residue, optimally combined with the optical #ow residue using linear discriminant analysis, determines the authenticity of the test image. An individual modeling method and a common modeling method are described. We also present a method to optimally choose the threshold for each subject for a multiple-subject authentication system. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of facial expression variations and registration errors. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.