Face recognition using neighborhood preserving projections

Authors:
Yanwei Pang;Nenghai Yu;Houqiang Li;Rong Zhang;Zhengkai Liu
Affiliations:
MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei, China
Venue:
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Year:
2005

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Abstract

Subspace learning is one of the main directions for face recognition. In this paper, a novel unsupervised subspace learning method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The central idea is to modify the classical locally linear embedding by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Experimental results on Yale face database and FERET face database show the effectiveness of the proposed method....