A semi-supervised support vector machine based algorithm for face recognition

Authors:
Wei-Shan Yang;Chun-Wei Tsai;Keng-Mao Cho;Chu-Sing Yang;Shou-Jen Lin;Ming-Chao Chiang
Affiliations:
Department of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C.;Department of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C.
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Most, if not all, of the researches in support vector machine (SVM) based face recognition algorithms have generally presumed that the classifier is static and thus unscalable, due to the fact that SVM is a supervised learning method. This paper introduces a novel SVM based face recognition method-by dynamically adding "new" faces of existing or new persons into the face database-which circumvents these difficulties. In other words, the proposed algorithm is able to learn and recognize faces that are not in the face database before. The paper presents the theory and the experimental results using the new approach. Our experimental results indicate that the accuracy rate of the proposed algorithm ranges from 91% up to 100% and outperforms all the others.