计算机工程与应用2016，Vol.52Issue(15)：24-28,5.DOI:10.3778/j.issn.1002-8331.1512-0376
基于Cost-Sensitive主成分分析的人脸识别
Face recognition based on Cost-Sensitive principal component analysis
摘要
Abstract
Existing face recognition algorithms aim to achieve high recognition accuracy, implicitly assuming that all mis-classifications lead to the same losses. This assumption, however, may not hold in the practical face recognition systems. Motivated by this concern, a new face recognition approach based on Cost-Sensitive Principal Component Analysis (Cost-Sensitive PCA)is proposed in this paper. It incorporates a cost sensitive function into Principal Component Analysis theory and determines the different loss cost by differentiating losses caused by different error recognition, which achieves more accurate face recognition. The experimental results on AR, FERET and UMIST face databases show that the pro-posed method achieves higher k nearest neighbor classification recognition accuracy with the least cost compared with the classical subspace-based face recognition methods.关键词
代价敏感/主成分分析/人脸识别/k最近邻Key words
Cost-Sensitive/Principal Component Analysis(PCA)/face recognition/k Nearest Neighbor(kNN)分类
信息技术与安全科学引用本文复制引用
谢晋,陈延东..基于Cost-Sensitive主成分分析的人脸识别[J].计算机工程与应用,2016,52(15):24-28,5.基金项目
湖北理工学院科研项目（No.13xjz05Q）；湖北理工学院校级重点科研项目（No.14xjz04A）；湖北省教育厅科学技术研究计划指导项目。 （）
