若干人脸识别算法的比较研究  

Comparative Study of Some Face Recognition Algorithms

作　　者：王婷[1] 杨国胜[1] 薛长松[1] 

机构地区：[1]河南大学先进控制与智能信息处理研究所,河南开封475001

出　　处：《河南大学学报（自然科学版）》2007年第2期191-194,共4页Journal of Henan University:Natural Science

基　　金：河南省自然科学基金资助项目(0523020600);河南省高校创新人才工程项目资助

摘　　要：对基于主元分析(PCA)、二维主元分析(2DPCA)和Fisher线性判别分析(FLDA)的人脸识别方法进行了比较研究.在分析了每一种方法的特点、应用场合和限制条件的基础上,提出了基于2DPCA和FLDA相结合的人脸识别算法.最后,利用AT&T人脸库,对基于这几种算法以及它们的组合算法的人脸识别方法进行了仿真比较实验,结果表明基于2DPCA的人脸识别方法识别精度高于基于PCA的方法;基于2DPCA和FLDA的人脸识别方法在保证分类精度的条件下提高了分类的速度,具有较好的识别性能.Some of face recognition methods based on Principal Component Analysis （PCA）, Two-dimensional Principal Component Analysis （2DPCA） and Fisher＇s Linear Discriminant Analysis （FLDA） are comparatively studied in this paper. On the basis of the analysis of characteristics, application occasion and limitations, a new method of face recognition based on combining 2DPCA and FLDA is proposed. Finally, comparison simulations are performed with the methods and their combined methods by means of AT＆T face database. The results show that the methods based on 2DPCA can have better recognition precision than the ones based on PCA; the method based on 2DPCA and FLDA improves the real-time characteristics in the condition of keeping the recognition precision and has the best performance.

关 键 词：主元分析 二维主元分析 FISHER线性判别分析 人脸识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]