In this paper, an approach to learn a robust sparse representation dictionary for face recognition is proposed. As well known, sparse representation algorithm can effectively tackle slight occlusion problems for face recognition. However, if images are corrupted by heavy noise, performance will be not guaranteed. In this paper, to enhance the robustness of sparse representation to serious noise in face images, we integrate low rank representation into dictionary learning to alleviate the influence of unfavorable factors such as large scale noise and occlusion. Among which we extract eigenfaces by singular value decomposition (SVD) from the low rank pictures to reduce dictionary atoms and, thereby, optimize the efficiency of improved algorithm. Otherwise, we characterize each image using the histogram of orientated gradient (HOG) feature which has been proven to be an effective descriptor for face recognition in particular. The performance of the proposed Low-rank and HOG feature based ESRC (LH_ESRC) algorithm on several popular face databases such as the Extended Yale B database and CMU_PIE face database shows the effectiveness of our method. In addition, we evaluate the robustness of our method by adding different proportions of randomly noise and block occlusion and real disgusts. Experimental results illustrate the benefits of our approach.
['Chun-Hou Zheng', 'Yi-Fu Hou', 'Jun Zhang']
Improved sparse representation with low-rank representation for robust face recognition