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Facial portraits matching by means of two-dimensional CCA and PLS

Kukharev G., Tujaka A., Formaczyński P.

Metody Informatyki Stosowanej

2010

nr 1 (22)

69-86

Paper presents implementation of the method of two-dimensional canonical correlation analysis and two-dimensional partial least squares applied to image matching. Both methods are based on representing the image as the sets of its rows and columns and implementation of CCA using these sets (hence we named the methods as CCArc and PLSrc). CCArc and PLSrc features simple implementation and lesser complexity than other known approaches. In applications to biometrics, CCArc and PLSrc are suitable to solving the problems when dimension of images (dimension of feature space) is greater than number of images, i.e. Small Sample Size problem (SSS). The paper demonstrates high efficiency of CCArc and PLSrc for a number of computer experiments using benchmark image databases.

Two-dimensional canonical correlation analysis for face image processing and recognition

Kukharev G., Kamenskaya E.

Metody Informatyki Stosowanej

2009

nr 3 (20)

103-112

Paper presents the method of two-dimensional canonical correlation analysis (2DCCA) as 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 these reason we named the method as CCArc). CCArc features simple implementation and lesser 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. Small Sample Size problem (SSS). Demonstrated high efficiency of CCArc method for a number of computer experiments. Experiments itself are described with compact notation allowing to use its results in the framework of meta-analysis.