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

摘要：

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 Cevikalps 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.

作者: Jun-bao LI Shu-chuan CHU Jeng-shyang PAN

作者单位: Harbin Institute of Technology, China Cheng Shiu University, Taiwan National Kaohsiung University of Applied Sciences, Taiwan

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）