东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2006, Vol. 27 ›› Issue (8): 847-850.DOI: -

• 论著 • 上一篇    下一篇

基于核主成分分析的人脸识别

赵丽红;孙宇舸;蔡玉;徐心和;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-23 修回日期:2013-06-23 出版日期:2006-08-15 发布日期:2013-06-23
  • 通讯作者: Zhao, L.-H.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60475036)

Face recognition based on kernel PCA

Zhao, Li-Hong (1); Sun, Yu-Ge (1); Cai, Yu (1); Xu, Xin-He (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-23 Revised:2013-06-23 Online:2006-08-15 Published:2013-06-23
  • Contact: Zhao, L.-H.
  • About author:-
  • Supported by:
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摘要： 核主成分分析方法是主成分分析的改进算法,其采用非线性方法提取主成分.把核主成分分析应用到人脸识别中,利用核主成分分析方法选择合适的核函数在高维空间提取人脸图像的主成分.核主成分分析与传统主成分分析相比,可以得到更好的适合分类的特征.基于ORL人脸库,识别核主成分分析提取出的主成分的相关性系数.实验结果表明,核主成分分析不仅实现了降维,而且能取得比传统主成分分析更好的识别性能,正确识别率为92.5%.

关键词: 特征抽取, 核主成分分析, 主成分分析, 人脸识别, 核函数

Abstract: KPCA extracting principal component with nonlinear method is an improved conventional PCA. The Kernel Principal Component Analysis (KPCA), is used in face recognition, which can make full use of the high correlation between different face images for feature extraction by selecting the proper kernel function. So KPCA can extract the feature set more suitable in categorization than classical conventional PCA. Based on ORL face database, recognizes correlation coefficients of principal component extracted by KPCA. Experimental results demonstrate that KPCA is not only good at dimensional reduction, but available to get better performance than conventional PCA, of which the correct recognition rate is up to 92.5%.

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