An adaptively weighted sub-pattern locality preserving projection for face recognition

Authors:
Jianzhong Wang;Baoxue Zhang;Shuyan Wang;Miao Qi;Jun Kong
Affiliations:
Computer School, Northeast Normal University, Changchun, China and School of Mathematics and Statistics, Northeast Normal University, Changchun, China;School of Mathematics and Statistics, Northeast Normal University, Changchun, China and Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Jingyue Street, Changchun, China;Computer School, Northeast Normal University, Changchun, China and Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Jingyue Street, Changchun, China;Computer School, Northeast Normal University, Changchun, China and Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Jingyue Street, Changchun, China;Computer School, Northeast Normal University, Changchun, China and Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Jingyue Street, Changchun, China
Venue:
Journal of Network and Computer Applications
Year:
2010

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Abstract

In this paper, an adaptively weighted sub-pattern locality preserving projection (Aw-SpLPP) algorithm is proposed for face recognition. Unlike the traditional LPP algorithm which operates directly on the whole face image patterns and obtains a global face features that best detects the essential face manifold structure, the proposed Aw-SpLPP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Furthermore, the contribution of each sub-pattern can be adaptively computed by Aw-SpLPP in order to enhance the robustness to facial pose, expression and illumination variations. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, YaleB and PIE). Experimental results show that Aw-SpLPP outperforms other holistic and sub-pattern based methods.