Face recognition using fast neighborhood component analysis with spatially smooth regularizer

Authors:
Faqiang Wang;Hongzhi Zhang;Wangmeng Zuo;Kuanquan Wang
Affiliations:
Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

For the robust recognition of noisy face images, this paper proposed an improved fast neighborhood component analysis (FNCA) method by introducing a spatially smooth regularizer (FNCA-SSR). The SSR can penalize large differences between adjacent pixels by enforcing local spatially smoothness, and makes FNCA-SSR model robust to Gaussian and pepper-salt noises in face image. Experimental results on the ORL and FERET face data sets show that, for the recognition of noisy face images, FNCA-SSR is very robust and can achieve much higher recognition accuracy than FNCA and other subspace methods.