Kernel-PCA for face recognition in different color spaces

Abstract

Different color spaces are better for different applications. This paper investigates the performance of face recognition with some color spaces using kernel-based Principal Component Analysis (Kernel-PCA). Kernel-PCA is a non-linear extension from the popular algorithm PCA. Experiments are performed with the Gaussian kernel function. Color spaces are linear or non-linear transform from RGB. In this paper, the RGB, YCbCr, and HSV color spaces are compared with the gray image (luminance information Y). Kernel-PCA is used to extract features from individual color components or from combining the three components of every color space in one vector. The experiments are performed on FEI color database. FEI database is frontal face images with seven profile images rotation of up to about 180 degrees and two different facial expression images. The experimental results show that the V color component of the HSV color space outperform all the used color organization.