首页 | 本学科首页   官方微博 | 高级检索  
     

巴氏距离与PCA结合的人脸识别
引用本文:熊建斌,王钦若,邓九英,刘奇,叶宝玉.巴氏距离与PCA结合的人脸识别[J].计算机工程与应用,2012,48(3):202-204.
作者姓名:熊建斌  王钦若  邓九英  刘奇  叶宝玉
作者单位:广东工业大学,广州,510006
基金项目:广东省重大科技专项(No.2009A080202006); 广东省自然科学基金资助项目(No.9151009001000021); 广东省教育部产学研合作专项资金资助项目(No.2009B090300341)
摘    要:利用巴氏距离(Bhattacharyya Distance)和PCA(Principal Component Analysis)相结合进行人脸识别研究,提出了使用巴氏距离和PCA相合的算法对特征进行提取。当特征向量维数高时,首先对样本K-L(Karhunen-Loeve)变换进行降维,然后采用巴氏距离特征的迭代算法,得到最小错误率上界。基于ORL人脸数据库的实验表明该方法的识别性能优于LDA、HPCA、HLDA,采用文中的算法可以有效地提高识别率,减少巴氏距离特征计算时间,具有较强的实用性。

关 键 词:巴氏距离  主分量分析  人脸识别  PCA和K-L变换相结合(PCA+K-L)
修稿时间: 

Study on face recognition based on Bhattacharyya distance and PCA method
XIONG Jianbin , WANG Qinruo , DENG Jiuying , LIU Qi , YE Baoyu.Study on face recognition based on Bhattacharyya distance and PCA method[J].Computer Engineering and Applications,2012,48(3):202-204.
Authors:XIONG Jianbin  WANG Qinruo  DENG Jiuying  LIU Qi  YE Baoyu
Affiliation:Guangdong University of Technology, Guangzhou 510006, China
Abstract:This paper studies the face recognition based on the methods of Bhattacharyya distance and principal component analysis, and proposes a smart feature selection method which combines principal component analysis and Bhattacharyya distance. When the feature vector dimension is high, the sample dimension is reduced by using K-L decomposition. It gets the smallest error rate upper bound by using iterative algorithm which has Bhattacharyya distance feature. The experiments in ORL face database shows that its performance is better than using the methods of LDA, HPCA and HLDA. This algorithm can raise the recognition rate effectively and reduce the time which is used to calculate the Bhattacharyya distance. It has strong practicability.
Keywords:Bhattacharyya distance  Principal Component Analysis(PCA)  face recognition  PCA+K-L(Principal Component Analysis and Karhunen-Loeve)
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号