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

Wei Shan Yang, Chun Wei Tsai, Keng Mao Cho, Chu Sing Yang, Shou Jen Lin, Ming Chao Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Pages1609-1614
Number of pages6
DOIs
Publication statusPublished - 2009 Dec 1
Event2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX, United States
Duration: 2009 Oct 112009 Oct 14

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
CountryUnited States
CitySan Antonio, TX
Period09-10-1109-10-14

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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