Regularized locality preserving projections and its extensions for face recognition

Authors:
Jiwen Lu;Yap-Peng Tan
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Year:
2010

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Abstract

We propose in this paper a parametric regularized locality preserving projections (LPP) method for face recognition. Our objective is to regulate the LPP space in a parametric manner and extract useful discriminant information from the whole feature space rather than a reduced projection subspace of principal component analysis. This results in better locality preserving power and higher recognition accuracy than the original LPP method. Moreover, the proposed regularization method can easily be extended to other manifold learning algorithms and to effectively address the small sample size problem. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.