无线电通信技术Issue(1)：73-75,98,4.DOI:10.3969/j.issn.1003-3114.2016.01.19
基于非相关多线性主成分分析的人脸识别算法
Face Recognition Algorithms Based on Uncorrelated Multilinear PCA
杨凌云 1秦岸2
作者信息
- 1. 安徽师范大学 物理与电子信息学院，安徽 芜湖241000
- 2. 重庆市公安局 巴南区分局科技信息化科，重庆400055
- 折叠
摘要
Abstract
In face recognition algorithms,the increase of feather dimensionality has always over⁃burdened the algorithm operation, so a new face recognition algorithm based on UMPCA and LDA is proposed.While the algorithm reduces the dimensionality,it remains the inner structure information as much as possible.UMPCA seeks a tensor⁃to⁃vector projection that captures most of the variation in the original tensorial input while obtaining uncorrelated features through successive variance maximization.A subset of features extracted is processed by classical LDA to find the best subspaces.Finally,the comprehensive experiments are provided on AT&T databases and the experiment results show its performance over other PCA plus LDA based algorithms.关键词
张量/非相关多线性主成分分析(UMPCA)/线性判别分析(LDA)/特征提取Key words
tensor object/uncorrelated multilinear principal component analysis/linear discriminant analysis/feature extraction分类
信息技术与安全科学引用本文复制引用
杨凌云,秦岸..基于非相关多线性主成分分析的人脸识别算法[J].无线电通信技术,2016,(1):73-75,98,4.
