Robust Face Recognition by Sparse Local Features from a Single Image under Occlusion

Occlusion and "one sample per person" are two challenging problems for face recognition and still not well solved till now. This paper investigates the two problems and proposes a novel method based on sparse local features to solve them. The contribution of our work is three-fold: first, the key characteristics of successful applying SIFT features for face recognition are analyzed. Second, based on the analysis of SIFT features, two new sparse local feature descriptors, namely Sparse HoG and Sparse LBP are proposed and they are combined together for extracting more discriminative features from an occluded and single image of one person. Third, a new matching strategy is proposed to measure the similarity between the testing and the gallery images. The proposed method is effective and efficient for solving the occlusion and 'one sample per person' problem. Experimental results on the AR database show that the proposed method outperforms the original SIFT, HoG, LBP based methods and also some other existing face recognition algorithms in terms of recognition accuracy.