Color space normalization: Enhancing the discriminating power of color spaces for face recognition

Jian Yang, Chengjun Liu, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

85 Citations (Scopus)

Abstract

This paper presents the concept of color space normalization (CSN) and two CSN techniques, i.e., the within-color-component normalization technique (CSN-I) and the across-color-component normalization technique (CSN-II), for enhancing the discriminating power of color spaces for face recognition. Different color spaces usually display different discriminating power, and our experiments on a large scale face recognition grand challenge (FRGC) problem reveal that the RGB and XYZ color spaces are weaker than the I1I2I3, YUV, YIQ, and LSLM color spaces for face recognition. We therefore apply our CSN techniques to normalize the weak color spaces, such as the RGB and the XYZ color spaces, the three hybrid color spaces XGB, YRB and ZRG, and 10 randomly generated color spaces. Experiments using the most challenging FRGC version 2 Experiment 4 with 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show that the proposed CSN techniques can significantly and consistently improve the discriminating power of the weak color spaces. Specifically, the normalized RGB, XYZ, XGB, and ZRG color spaces are more effective than or as effective as the I1I2I3, YUV, YIQ and LSLM color spaces for face recognition. The additional experiments using the AR database validate the generalization of the proposed CSN techniques. We finally explain why the CSN techniques can improve the recognition performance of color spaces from the color component correlation point of view.
Original languageEnglish
Pages (from-to)1454-1466
Number of pages13
JournalPattern Recognition
Volume43
Issue number4
DOIs
Publication statusPublished - 1 Apr 2010

Keywords

  • Biometrics
  • Color model
  • Color space
  • Face recognition
  • Face recognition grand challenge (FRGC)
  • Fisher linear discriminant analysis (FLD or LDA)
  • Pattern recognition

ASJC Scopus subject areas

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

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