Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27903
Title: Color image canonical correlation analysis for face feature extraction and recognition
Authors: Jing, X
Li, S
Lan, C
Zhang, D 
Yang, J
Liu, Q
Keywords: Canonical correlation analysis (CCA)
Color face recognition
Color image CCA (CICCA)
Feature extraction
Issue Date: 2011
Publisher: Elsevier
Source: Signal processing, 2011, v. 91, no. 8, p. 2132-2140 How to cite?
Journal: Signal processing 
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.
URI: http://hdl.handle.net/10397/27903
ISSN: 0165-1684
EISSN: 1872-7557
DOI: 10.1016/j.sigpro.2011.02.016
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