A novel illumination normalization method in face recognition based on logarithmic total variation

Abstract

Varying illumination is a tricky issue in face recognition. In this paper, we make improvement on the logarithmic total variation (LTV) algorithm to handle the varying illumination in face image. First of all, logarithmic total variation (LTV) is adopt to separate the face image into high-frequency and low-frequency features. Then, a novel illumination normalization method is proposed to handle low-frequency feature, which is founded on the advanced contrast limited adaptive histogram equalization (CLAHE). Furthermore, threshold-value filtering is utilized to realize enhancement on high-frequency feature. Finally, the normalized face image can take shape through the normalized high-frequency feature and enhanced low-frequency feature. We make comparative experiments on YALE B databases, including three types of techniques. The finnal results show that CLA and TH-LTV algorithm owns excellent recognition performance compared to other state-of-art algorithms.