Fast communication: Color image canonical correlation analysis for face feature extraction and recognition

Authors:
Xiaoyuan Jing;Sheng Li;Chao Lan;David Zhang;Jingyu Yang;Qian Liu
Affiliations:
College of Automation, Nanjing University of Posts and Telecommunications, PR China and State Key Laboratory for Novel Software Technology, Nanjing University, PR China;College of Automation, Nanjing University of Posts and Telecommunications, PR China;College of Automation, Nanjing University of Posts and Telecommunications, PR China;Department of Computing, Hong Kong Polytechnic University, Hong Kong;College of Computer Science, Nanjing University of Science and Technology, PR China;College of Automation, Nanjing University of Posts and Telecommunications, PR China
Venue:
Signal Processing
Year:
2011

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Abstract

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.