A structure-preserved local matching approach for face recognition

Authors:
Jianzhong Wang;Zhiqiang Ma;Baoxue Zhang;Miao Qi;Jun Kong
Affiliations:
School of Computer Science and Information Technology, Northeast Normal University, Changchun, China and National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal ...;School of Computer Science and Information Technology, Northeast Normal University, Changchun, China;School of Mathematics and Statistics, Northeast Normal University, Changchun, China;School of Computer Science and Information Technology, Northeast Normal University, Changchun, China;School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.