Recognition of Faces Using Texture-based Principal Component Analysis and Grassmannian distances analysis

摘要：

This paper introduces a new face recognition method-texture-based Principal Component Analysis (PCA), which employs PCA on texture features.Initially, the eigenspace of texture images is created by eigenvalues and eigenvectors.From this space,the eigentextures are constructed.and most of the eigentextures are selected by using PCA.With these eigentextures, we generalize Grassmannian distances into texture feature space to recognize.We address the problem of face recognition in terms of the subject-specific subspaces instead of image vectors.The proposed method is tested on Essex Face 94 database,and it has been demenstrated to have a promising performance.

关键词： face recognition principal component analysis(PCA) texture features Grassmannian distance analysis

作者: Bei Ma Hailin Zhang

作者单位: State Key Lab of ISN Xidian University Xian,China

会议类型: 国际会议

会议名称: 2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)

会议地点: 长沙

会议语种:英文

页码: 355-359

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）