KERNEL BASED 2D SYMMETRICAL PRINCIPAL COMPONENT ANALYSIS FOR FACE CLASSIFICATION

摘要：

This paper presents a novel algorithm-Kernel based 2D Symmetrical Principal Component Analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert Space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.

关键词： Feature eztraction kernel principal component analysis symmetrical principal component analysis two-dimensional principal component analysis kernel based two-dimensional symmetrical principal component analysis

作者: CONG-DE LU YU-LEI CHEN BIN-BIN HE

作者单位: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu School of Information Engineering, Chengdu University of Technology, Chengdu 610059, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 442-447

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）