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应用科学学报 ›› 2005, Vol. 23 ›› Issue (6): 551-556.

• 论文 •    下一篇

基于KPCA及最佳鉴别独立分量的人脸识别方法

贺云辉, 赵力, 邹采荣   

  1. 东南大学无线电工程系, 江苏南京 210096
  • 收稿日期:2004-08-21 修回日期:2004-10-24 出版日期:2005-11-30 发布日期:2005-11-30
  • 作者简介:贺云辉(1975-),男,河北武安人,博士生,E-mail:heyunhui@seu.edu.cn;赵力(1958-),男,江苏南京人,教授,博导,E-mail:zhaoli@seu.edu.cn;邹采荣(1963-),男,江苏昆山人,教授,博导,E-mail:cairong@seu.edu.cn

Face Recognition Based on KPCA and Optimal Discriminant Independent Components

HE Yun-hui, ZHAO Li, ZOU Cai-rong   

  1. Department of Radio Engineering, Southeast University, Nanjing 210096, China
  • Received:2004-08-21 Revised:2004-10-24 Online:2005-11-30 Published:2005-11-30

PDF

76

被引次数

21

摘要： 首先分析了独立分量分析(ICA)在人脸识别应用中存在的一些问题,然后从3个方面对基于独立分量分析的人脸识别方法进行了改进:首先利用KPCA将人脸映射到特征空间,在特征空间进行ICA得到相对于原样本的非线性独立分量,从而得到一种非线性独立分量分析的方法;其次,定义了Fisher鉴别信息作为选取最佳鉴别独立分量的准则;最后,提出了一种用最佳独立分量表示待识别人脸图像的方法,克服了用直接投影得到的特征不准确的问题.基于ORL人脸数据库的实验表明,利用此改进的非线性最佳鉴别ICA方法,可以得到优于FLDA方法的识别性能,且在特征数较少时仍能得到较好的识别稳定性.

关键词: 最佳鉴别分析, 人脸识别, 独立分量分析, 核主分量分析

Abstract: Independent component analysis (ICA) is a generalization of principle component analysis (PCA), which encounters some problems in face recognition.These problems are analyzed and three approaches proposed to improve the performance of ICA-based face recognition.First, a kernel based ICA method with KPCA is presented, which maps samples onto the feature space and obtains nonlinear independent components.Second, Fisher discriminant information is defined to select optimal discriminant independent components.Finally, a method is proposed to obtain the optimal feature representation using optimal discriminant independent components for unknown face samples.Experiments using ORL database indicate that the improved method outperforms FLDA, with recognition stability better than FLDA in case the number of features is small.

Key words: independent component analysis, face recognition, kernel principle component analysis, optimal discriminant analysis

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