Sparse Representation and Low-Rank Approximation for Robust Face Recognition

Face recognition under various conditions such as illumination, poses, expression, and occlusion has been one of the most challenging problems in computer vision. Over the last few years there has been significant attention paid to the low-rank approximation (LRA) and sparse representation (SR) techniques. The applications of these techniques have appeared in many different areas ranging from handwritten character recognition to multi-factor face recognition. In this paper, we will review some of the most recent works using LRA and SR in the multi-factor face recognition problem, and present a novel framework to improve their performance in the recognition of faces under various affecting conditions. Our results are comparable to or better than the state-of-the-art in this area.