Comparative Study on Color Components for PCA-Based Face Recognition

Dongzhu Yin, Yoshihiro Sugaya, Shinichiro Omachi, Hirotomo Aso

Research output: Contribution to journalArticlepeer-review


Using color information can significantly improve the face recognition rate instead of using the grayscale luminance image. However, there are few works that try to compare the color space models on face recognition. In this paper, we investigate thirty different color space models on face recognition using the classical principal component analysis (PCA). Through the extensive experiments we find that after successfully diminishing the influence of the illumination the recognition accuracy can be improved by 4.6~5.5 percent points.

Original languageEnglish
Pages (from-to)671-678
Number of pages8
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number4
Publication statusPublished - 2011 Jan


  • color space
  • face recognition
  • principal component analysis


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