Pattern Recognition and Image Analysis

, Volume 20, Issue 2, pp 210–219

Application of two-dimensional canonical correlation analysis for face image processing and recognition


    • West Pomeranian University of Technology
    • St.-Petersburg Electrotechnical University
  • E. Kamenskaya
    • St.-Petersburg Electrotechnical University
Applications Problems

DOI: 10.1134/S1054661810020136

Cite this article as:
Kukharev, G. & Kamenskaya, E. Pattern Recognit. Image Anal. (2010) 20: 210. doi:10.1134/S1054661810020136


Paper presents the method of two-dimensional canonical correlation analysis (2DCCA) applied to image processing and biometrics. Method is based on representing the image as the sets of its rows (r) and columns (c) and implementation of CCA using these sets (for this reason we named the method as CCArc). CCArc features simple implementation and lower complexity than other known approaches. In applications to biometrics CCArc is suitable to solving the problems when dimension of images (dimension of feature space) is greater than number of images, i.e. when Small Sample Size (SSS) problem exists.

High efficiency of CCArc method is demonstrated for a number of computer experiments. Experiments are described by means of compact notations that simplify use of results in the framework of meta-analysis.


two-dimensional canonical correlation analysis (2DCCA)space of canonical variablesfeature space dimensionality reductionbiometrics

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© Pleiades Publishing, Ltd. 2010