Orthogonal Discriminant Neighborhood Preserving Projections for Face Recognition

摘要：

Subspace learning is one of the main directions for face recognition.In this paper,a novel subspace learning approach,called Orthogonal Discriminant Neighborhood Preserving Projections(ODNPP),is proposed for robust face recognition.The aim of ODNPP is to preserve the within-class geometric structure,while maximizing the betweenclass scatter.In order to improve the discriminating power,Schur decomposition is used to obtain the orthogonal basis eigenvectors. Experiment results on ORL face database and Yale face database demonstrate the effectiveness and robustness of the proposed method.

关键词： face recognition subspace learning within-class geometric structure between-class scatter Schnr decomposition

作者: Guoqiang Wang Xiaojing Hou

作者单位: Department of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 649-652

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）