Two-Dimensional Canonical Correlation Analysis

In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where we extend the standard CCA in such a way that relations between two different sets of image data are directly sought without reshaping images into vectors. We stress that 2D-CCA dramatically reduces the computational complexity, compared to the standard CCA. We show the useful behavior of 2D-CCA through numerical examples of correspondence learning between face images in different poses and illumination conditions.