Facial Texture Feature Based Face Recognition with Common Vector Analysis in the Kernel Space

A novel face recognition method based on facial texture feature with common vector analysis is presented in this paper. The novelty of this paper comes from (1) facial texture feature characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination and facial expressions is extracted by Gabor wavelet, which improves the recognition performance; (2) This paper formulates Cevikalp´s discriminative common vector (DCV) method from space isomorphic mapping view in the kernel-inducing feature space and develops a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus DCV. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. Based on the above ideas, we propose a novel face recognition method, namely kernel common Gaborfaces method, by extracting the facial texture feature using Gabor wavelet and classification using the proposed kernel common vector analysis algorithm, whose effectiveness is tested on ORL and Yale face databases.