Extracting discriminative color features for face recognition

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27 Scopus citations

Abstract

This paper presents a discriminative color features (DCF) method, which applies a simple yet effective color model, a novel similarity measure, and effective color feature extraction methods, for improving face recognition performance. First, the new color model is constructed according to the principle of Ockham's razor from a number of available models that take advantage of the subtraction of the primary colors for boosting pattern recognition performance. Second, the novel similarity measure integrates both the angular and the distance information for improving upon the broadly applied similarity measures. Finally, the discriminative color features are extracted from a compact color image representation by means of discriminant analysis with enhanced generalization capabilities. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show the feasibility of the proposed method.

Original languageEnglish (US)
Pages (from-to)1796-1804
Number of pages9
JournalPattern Recognition Letters
Volume32
Issue number14
DOIs
StatePublished - Oct 15 2011

All Science Journal Classification (ASJC) codes

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

Keywords

  • Discriminant analysis
  • Discriminative color features (DCF) method
  • Face Recognition Grand Challenge (FRGC)
  • Face recognition
  • Novel similarity measure

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