Face Recognition under Varying Lighting Based on the Probabilistic Model of Gabor Phase
Laiyun Qing, Shiguang Shan, Xilin Chen, Wen Gao · 2006
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 [1] 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.