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.