Occlusion pattern-based dictionary for robust face recognition

Cho-Ying Wu, Jian-Jiun Ding

Abstract: Robust sparse representation has been applied to tackle some challenging problems in face recognition. In this paper, we propose a new method called occlusion pattern based sparse representation classification (OPSRC). First we find the contiguous occlusion area in the query image to create an occlusion pattern. Then, we add the occlusion pattern to all face images in the face image dictionary, resulting in an occlusion dictionary. The original dictionary and occlusion dictionary are solved together. By assuming some frequently occluded parts of the face and using additive form of half-quadratic optimization that has good performance of error detection, we create occlusion patterns and construct a dictionary robust to occlusion, especially in the case of per sample of each person. Our proposed method OPSRC outperforms other benchmark methods, revealing its potential on surveillance and security application.