Multi-class support vector machines with case-based combination for face recognition

Jaepil Ko, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

The support vector machine is basically to deal with a two-class classification problem. To get M-class classifiers for face recognition, it is common to construct a set of binary classifiers f1,....fM, each trained to separate one class from the rest. The multi-class classification method has a main shortcoming that the binary classifiers used are obtained by training on different binary classification problems, and thus it is unclear whether their real-valued outputs are on comparable scales. In this paper, we try to use additional information, relative outputs of the machines, for final decision. We propose case-based combination with reject option to use the information. The experiments on the ORL face database shows that the proposed method achieves a slight better performance than the previous multi-class support vector machines.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsNicolai Petkov, Michel A. Westenberg
PublisherSpringer Verlag
Pages623-629
Number of pages7
ISBN (Print)3540407308, 9783540407300
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2756
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ko, J., & Byun, H. (2003). Multi-class support vector machines with case-based combination for face recognition. In N. Petkov, & M. A. Westenberg (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 623-629). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2756). Springer Verlag. https://doi.org/10.1007/978-3-540-45179-2_76