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• [P552] A Semi-Supervised Support Vector Machine Based Algorithm for Face Recognition
  Author(s): Chun-Wei Tsai, Chu-Sing Yang, Ming-Chao Chiang, Yang Wei-Shan, Keng-Mao Cho, Shou-Jen Lin
  
  

  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.

  
   

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