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Face Recognition Using A Low Rank Representation Based Projections Method

Authors:	Zhenyu?Wang,Wankou?Yang author-information" > author-information__contact u-icon-before" > mailto:wankou.yang@yahoo.com" title=" wankou.yang@yahoo.com" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author,Fumin?Shen

Affiliation:	1.School of Automation,Southeast University,Nanjing,China;2.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education,Southeast University,Nanjing,China;3.Key Laboratory of Child Development and Learning Science of Ministry of Education,Southeast University,Nanjing,China;4.School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,China

Abstract:	In this paper, a low rank representation based projections (LRRP) method is presented for face recognition. In LRRP, low rank representation is used to construct a nuclear graph to characterize the local compactness information by designing the local scatter matrix like SPP; the total separability information is characterized by the total scatter like PCA. LRRP seeks the projection matrix simultaneously maximizing the total separability and the local compactness. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that LRRP works well for face recognition.

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