Research on KPCA and NS-LDA combined face recognition

摘要：

Kernel Principal Component Analysis (KPCA) is the promotion ofPCA in kernel space, Null space LDA can be directly employed to choose a set of optimal projection vectors by preserving effective information of null space of within-class scatter maximizing ratio of the between-class scatter to the within-class scatter. This paper puts forward the method about KPCA plus NS-LDA for feature extraction and is applied in face recognition study, it enhances face recognition performance by virtue of combining the advantages of KPCA makes use of data high order characteristic and good divisibility of NS-LDA projection matrix. The experimental results show this method could effectively improve the recognition rate.

关键词： component Face recognition Kernel Principal Component Analysis (KPCA) Null Space Linear Discriminant Analysis (NS-LDA) Cosine angle distance

作者: Lei Zhao Jiwen Dong Xiuli Li

作者单位: School of Information Science and Engineering, University of Jinan,Shandong Provincial Key Laboratory of Network based Intelligent Computing,Jinan 250022, China

会议类型: 国际会议

会议名称: 2012 Fifth International Symposium on Computational Intelligence and Design 第五届计算智能与设计国际会议 ISCID 2012

会议地点: 杭州

会议语种:英文

页码: 140-143

在线出版日期: 2012-10-28（万方平台首次上网日期，不代表论文的发表时间）