Fusion of two different PCAs for face recognition

doi:10.3778/j.issn.1002-8331.2010.01.057

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (1): 194-195.DOI: 10.3778/j.issn.1002-8331.2010.01.057

• 图形、图像、模式识别 • Previous Articles Next Articles

Fusion of two different PCAs for face recognition

YANG Jun^1，2，ZHANG Xiu-qiong²，GAO Zhi-sheng²，YUAN Hong-zhao²

1.College of Computer Science，Sichuan Normal University，Chengdu 610066，China
2.Institute of Image & Graphic，Sichuan University，Chengdu 610064，China

Received:2008-07-21 Revised:2008-08-25 Online:2010-01-01 Published:2010-01-01
Contact: YANG Jun

用于人脸识别的两类主成分分析融合

杨军^1，2，张秀琼²，高志升²，袁红照²

1.四川师范大学计算机科学学院，成都 610066
2.四川大学图形图像研究所，成都 610064

通讯作者: 杨军

Abstract

Abstract: The principle of two different PCAs，PCA based on global scatter matrix and PCA based on global between-class scatter matrix is analyzed firstly.Two different fusion methods，feature level fusion and decision level fusion are proposed using the feature got from two different PCAs.The experiment result is displayed and data fusion method is proved to be efficient for getting better recognition rate.

Key words: face recognition, Principal Component Analysis（PCA）, global scatter matrix, between-class scatter matrix, data fusion

摘要： 分析了基于总体离散度矩阵和总类间离散度矩阵的主成分分析的原理。利用两种方法分别提取人脸特征并进行识别。对两种方法获得的结果进行了特征层融合和决策层融合，基于ORL人脸数据库的实验表明该方法的识别性能优于单一的主成分分析方法。

关键词: 人脸识别, 主成分分析, 总体离散度矩阵, 类间离散度矩阵, 数据融合

CLC Number:

TP391

YANG Jun^1，2，ZHANG Xiu-qiong²，GAO Zhi-sheng²，YUAN Hong-zhao². Fusion of two different PCAs for face recognition[J]. Computer Engineering and Applications, 2010, 46(1): 194-195.

杨军^1，2，张秀琼²，高志升²，袁红照². 用于人脸识别的两类主成分分析融合[J]. 计算机工程与应用, 2010, 46(1): 194-195.

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Fusion of two different PCAs for face recognition

用于人脸识别的两类主成分分析融合

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