Using Gabor Filters Features for Multi-Pose Face Recognition in Color Images

Abstract

Human face recognition plays an important role in applications such as video surveillance, human computer interface, and face image database management. This paper presents an improved face recognition method for multi-pose face recognition in color images, which addresses the problems of illumination and pose variation. At first, color multi-pose faces image features were extracted based on Gabor wavelet with different orientations and scales filters, then the mean and standard deviation of the filtering image output are computed as features for face recognition. In addition, these features were fed up into support vector machine (SVM) for face recognition. Experimental results show that successful face recognition over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from stereo-pair database.