Regularized discriminant analysis for face recognition

Itzik Pima, Mayer Aladjem

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This paper studies regularized discriminant analysis (RDA) in the context of face recognition. We check RDA sensitivity to different photometric preprocessing methods and compare its performance to other classifiers. Our study shows that RDA is better able to extract the relevant discriminatory information from training data than the other classifiers tested, thus obtaining a lower error rate. Moreover, RDA is robust under various lighting conditions while the other classifiers perform badly when no photometric method is applied.

Original languageEnglish
Pages (from-to)1945-1948
Number of pages4
JournalPattern Recognition
Volume37
Issue number9
DOIs
StatePublished - 1 Sep 2004

Keywords

  • Discriminant analysis
  • Face recognition
  • Feature extraction
  • Photometric preprocessing
  • Principal component analysis
  • Regularization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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