Robust semi-supervised learning for biometrics

Authors:
Nanhai Yang;Mingming Huang;Ran He;Xiukun Wang
Affiliations:
Department of Computer Science and Technology, Dalian University of Technology, Dalian, China;Department of Computer Science and Technology, Dalian University of Technology, Dalian, China;Department of Computer Science and Technology, Dalian University of Technology, Dalian, China;Department of Computer Science and Technology, Dalian University of Technology, Dalian, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Year:
2010

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Abstract

To deal with the problem of sensitivity to noise in semi-supervised learning for biometrics, this paper proposes a robust Gaussian-Laplacian Regularized (GLR) framework based on maximum correntropy criterion (MCC), called GLR-MCC, along with its convergence analysis. The half quadratic (HQ) optimization technique is used to simplify the correntropy optimization problem to a standard semi-supervised problem in each iteration. Experimental results show that the proposed GRL-MCC can effectively improve the semi-supervised learning performance and is robust to mislabeling noise and occlusion as compared with GLR.