Color image canonical correlation analysis for face feature extraction and recognition

Xiaoyuan Jing, Sheng Li, Chao Lan, Dapeng Zhang, Jingyu Yang, Qian Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.
Original languageEnglish
Pages (from-to)2132-2140
Number of pages9
JournalSignal Processing
Issue number8
Publication statusPublished - 1 Aug 2011


  • Canonical correlation analysis (CCA)
  • Color face recognition
  • Color image CCA (CICCA)
  • Feature extraction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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