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SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

YANWEI PANG

Information Processing Center, University of Science and Technology of China, Hefei 230027, P. R. China

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ZHENGKAI LIU

Information Processing Center, University of Science and Technology of China, Hefei 230027, P. R. China

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YUEFANG SUN

Faculty of Economics and Management, Yanshan University, Qinhuangdao 066004, P. R. China

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https://doi.org/10.1142/S0219878906000812Cited by:0 (Source: Crossref)

Abstract

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.

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