计算机工程与应用Issue(12)：120-124,5.DOI:10.3778/j.issn.1002-8331.1206-0144
基于核正交半监督鉴别分析的人脸识别算法
Face recognition algorithm based on kernel orthogonal semi-supervised dis- criminant analysis
摘要
Abstract
In view of the problems of nonlinear feature extraction and use of a few labeled samples in face recognition, a new algorithm of orthogonal optimal semi-supervised discriminant vectors in a kernel space is proposed. Nonlinear kernel mapping is used to map the face data into an implicit feature space. In this space, the MFA can make use of small amount of labeled samples and the UDP can study a large number of unlabeled samples. The object function is defined using the semi-supervised method. Then optimal projection vector is found using orthogonal approach and face recognition is realized. The effectiveness of the proposed methods is validated through the experimental results on ORL and YALE face databases.关键词
边界Fisher判别分析/无监督鉴别投影/半监督/核空间/人脸识别Key words
Marginal Fisher Analysis(MFA)/Unsupervised Discriminant Projection(UDP)/semi-supervised/kernel space/face recognition分类
信息技术与安全科学引用本文复制引用
王燕,刘花丽,苏文君..基于核正交半监督鉴别分析的人脸识别算法[J].计算机工程与应用,2014,(12):120-124,5.基金项目
甘肃省自然科学基金（No.1014RJZA009，No.1112RJZA029）；甘肃省高等学校基本科研业务费项目（No.1114ZTC144）。 （）
