Lighting Aware Preprocessing for Face Recognition across Varying Illumination

Abstract

Illumination variation is one of intractable yet crucial problems in face recognition and many lighting normalization approaches have been proposed in the past decades. Nevertheless, most of them preprocess all the face images in the same way thus without considering the specific lighting in each face image. In this paper, we propose a lighting aware preprocessing (LAP) method, which performs adaptive preprocessing for each testing image according to its lighting attribute. Specifically, the lighting attribute of a testing face image is first estimated by using spherical harmonic model. Then, a von Mises-Fisher (vMF) distribution learnt from a training set is exploited to model the probability that the estimated lighting belongs to normal lighting. Based on this probability, adaptive preprocessing is performed to normalize the lighting variation in the input image. Extensive experiments on Extended YaleB and Multi-PIE face databases show the effectiveness of our proposed method.