Face authentication for multiple subjects using eigenflow

摘要

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 eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and an image in the training set are first computed. The optical flow is then fitted to a model that is pre-trained by applying principal component analysis to optical flows resulting from facial expressions and registration errors for the subject. The eigenflow residue, optimally combined with the optical flow 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.