[1]彭春华,刘 刚.基于粗糙集的人脸识别改进方法[J].江西师范大学学报(自然科学版),2016,40(05):487-491.
　PENG Chunhua,LIU Gang.The Improved Method for Face Recognition Based On Rough Set[J].Journal of Jiangxi Normal University:Natural Science Edition,2016,40(05):487-491.
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基于粗糙集的人脸识别改进方法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2016年05期
页码:
487-491
栏目:
出版日期:
2016-10-01

文章信息/Info

Title:
The Improved Method for Face Recognition Based On Rough Set
作者:
彭春华; 刘 刚
江西师范大学物理与通信电子学院,江西 南昌 330022
Author(s):
PENG Chunhua; LIU Gang
College of Physics and Communication Electronics,Jiangxi Normal University,Nanchang Jiangxi 330022,China
关键词:
人脸识别;  主元分析法;  粗糙集;  特征子空间
Keywords:
face recognition;  principal component analysis;  rough set;  feature subspace
分类号:
TP 391
摘要:
人脸识别因其高效、安全和非接触性的特点,在公共信息安全领域得到了广泛应用.针对传统主元分析方法(PCA)和随机主元分析法(Random PCA)在实际应用中存在抗干扰能力差、识别率不高以及2种方法特征融合后计算复杂的问题,提出了一种基于随机主成分分析+粗糙集(Random PCA+rough set)的人脸识别方法.该方法用PCA提取人脸的全局特征,用Random PCA提取人脸图像的局部特征,再将这2种特征通过串联的方式构建特征子空间.在特征子空间里用粗糙集去提取最具区分度的特征,从而有效减少了分类时的计算复杂度并提高了识别率.实验结果表明:该方法较传统PCA方法的识别率和识别时间分别提高了7.09%和6.06%.
Abstract:
Face recognition has been widely used in the field of public information security due to it’s high efficiency,safety,and non-contact.The problems of traditional methods principal component analysis(PCA)and Random principal component analysis(Random PCA)is lack of anti-interference and low recognition rate,otherwise the problem of in fusion of PCA and Random PCA is the calculate time is too long.In order to solve these problems,a method based on Random PCA plus rough set is proposed for face recognition.The method first exploit PCA and Random PCA extract the global feature and local feature,respectively.And then cascade the global feature and the local feature to construct the feature subspace.At last exploit rough set to extract the most distinguish feature from the feature subspace,therefore the method can improve the ability of anti-interference and recognition rate.Compared with the traditional method PCA,the results show that the recognition rate and recognition time improved 7.09% and 6.06%,respectively.

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　ZHAO Qing-min,OUYANG Huan,GU Dao-ping.Application Study on Curvelet Transform in Face Recognition[J].Journal of Jiangxi Normal University:Natural Science Edition,2013,(05):397.

备注/Memo

备注/Memo:
收稿日期:2015-11-27基金项目:江西省科技计划(20133BBE50035)资助项目.通信作者:刘 刚(1970-),男,江西泰和人,副教授,主要从事传感器与微控制系统的研究.
更新日期/Last Update: 1900-01-01