计算机工程与应用2017，Vol.53Issue(6)：1-6,6.DOI:10.3778/j.issn.1002-8331.1608-0390
基于张量的2D-PCA人脸识别算法
Novel 2D-PCA face recognition based on tensor
摘要
Abstract
The color of facial images is one of the important features in face recognition, but the existing color face recogni-tion based on 2D-PCA ignores its spatial relation. Therefore, a novel approach called Tensor PCA which uses a 3rd-order tensor to represent an RGB color image is proposed. In order to achieve the best classification, it seeks three projection matrices which consist of the eigenvectors corresponding to the largest eigenvalues of the n-mode total scatter matrix to maximize the distance of the projected samples, and constructs an ALS iterative procedure to optimize the projection matrices. As is shown in the results on Georgia Tech face database, in contrast with the process of 2D-PCA the recognition rate increases by 5.53%and the training time decreases by 78.1%.关键词
人脸识别/色彩信息/二维主成分分析(2D-PCA)/张量Key words
face recognition/color information/two-Dimensional Principal Component Analysis(2D-PCA)/tensor分类
信息技术与安全科学引用本文复制引用
叶学义,王大安,宦天枢,夏经文,顾亚风..基于张量的2D-PCA人脸识别算法[J].计算机工程与应用,2017,53(6):1-6,6.基金项目
国家自然科学基金青年基金资助项目(No.60802047). （No.60802047）
