A New Kernel Orthogonal Projection Analysis Approach for Face Recognition

摘要：

　　In the field of face recognition,how to extract effective nonlinear discriminative features is an important research topic.In this paper,we propose a new kernel orthogonal projection analysis approach.We obtain the optimal nonlinear projective vector which can differentiate one class and its adjacent classes,by using the Fisher criterion and constructing the specific between-class and within-class scatter matrices in kernel space.In addition,to eliminate the redundancy among projective vectors,our approach makes every projective vector satisfy locally orthogonal constraints by using the corresponding class and part of its most adjacent classes.Experimental results on the public AR and CAS-PEAL face databases demonstrate that the proposed approach outperforms several representative nonlinear projection analysis methods.

关键词： kernel orthogonal projection analysis feature extraction locally orthogonal constraints face recognition

作者: Xiaoyuan Jing Min Li Yongfang Yao Songhao Zhu Sheng Li

作者单位: College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China;State Key Lab College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China

会议类型: 国际会议

会议名称: 2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

会议地点: 郑州

会议语种:英文

页码: 1165-1170

在线出版日期: 2013-10-19（万方平台首次上网日期，不代表论文的发表时间）