Authors: Georgy Kukharev, Andrzej Tujaka, Pawel Forczmanski
Addresses: Department of Computer Software Environment, Saint Petersburg Electrotechnical University 'LETI', St. Petersburg, Russia. ' Faculty of Computer Science and Information Technologies, West Pomeranian University of Technology, Szczecin, ' Faculty of Computer Science and Information Technologies, West Pomeranian University of Technology, Szczecin,
Abstract: This paper presents the implementation of the method of twodimensional Canonical Correlation Analysis (CCA) and two-dimensional Partial Least Squares (PLS) 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 feature 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 the number of images, i.e., Small Sample Size (SSS) problem. This paper demonstrates high efficiency of CCArc and PLSrc for a number of computer experiments, using benchmark image databases.
Keywords: CCArc; 2D canonical correlation analysis; PLSrc; 2D partial least squares; feature space dimensionality reduction; face matching; face recognition; biometrics.
International Journal of Biometrics, 2011 Vol.3 No.4, pp.300 - 321
Published online: 24 Jan 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article