Face Recognition under Varying Lighting Based on the Probabilistic Model of Gabor Phase

This paper present a novel method for robust illumination-tolerant face recognition based on the Gabor phase and a probabilistic similarity measure. Invited by the work in Eigenphases of J. Lou, et al. (2002) by using the phase spectrum of face images, we use the phase information of the multi-resolution and multi-orientation Gabor filters. We show that the Gabor phase has more discriminative information and it is tolerate to illumination variations. Then we use a probabilistic similarity measure based on a Bayesian (MAP) analysis of the difference between the Gabor phases of two face images. We train the model using some images in the illumination subset of CMU-PIE database and test on the other images of CMU-PIE database and the Yale B database and get comparative results