SVM-based discriminant analysis for face recognition

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

Abstract

In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.

Original languageEnglish
Title of host publication2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
Pages2112-2115
Number of pages4
DOIs
Publication statusPublished - 2008 Sep 23
Event2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008 - Singapore, Singapore
Duration: 2008 Jun 32008 Jun 5

Publication series

Name2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008

Other

Other2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
CountrySingapore
CitySingapore
Period08/6/308/6/5

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

Cite this

Kim, S. K., Toh, K. A., & Lee, S. (2008). SVM-based discriminant analysis for face recognition. In 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008 (pp. 2112-2115). [4582892] (2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008). https://doi.org/10.1109/ICIEA.2008.4582892