Parts-Based holistic face recognition with RBF neural networks

Authors:
Wei Zhou;Xiaorong Pu;Ziming Zheng
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

This paper proposes a method for face recognition by integrating non-negative matrix factorization with sparseness constraints (NMFs) and radial basis function (RBF) classifier. NMFs can represent a facial image based on either local or holistic features by constraining the sparseness of the basis images. The comparative experiments are carried out between NMFs with low or high sparseness and principle component analysis (PCA) for recognizing faces with or without occlusions. The simulation results show that RBF classifier outperforms k–nearest neighbor linear classifier significantly in recognizing faces with occlusions, and the holistic representations are generally less sensitive to occlusions or noise than parts-based representations.