Illumination Normalization for Face Recognition via Jointly Optimized Dictionary-Learning and Sparse Representation

Yuyao Zhang, Lihui Wang, Xiaojun Guan, Hongjiang Wei

Abstract: In this paper, we present a novel patch-based dictionary learning (DL) framework for face illumination normalization (IN). The proposed method is based on sparse representation that is from the coupled dictionaries (DL-IN). The used dictionary pairs in this paper are jointly optimized from paired face images with normal and irregular illuminations. A Gaussian mixture model (GMM) clustering was further used to improve the capability of modeling data with more complex distributions. The proposed GMM adapts individual model based on a portion of the samples and lastly fuses all the images together. Experimental results show that the proposed framework by jointly optimizing DL and incorporating structural sparsity as a prior can improve the accuracy for face recognition.