{"title":"Two Dimension Locally Principal Component Analysis for Face Recognition","authors":"Yu-sheng Lin, Jianguo Wang, Jing-yu Yang","doi":"10.1109/CCPR.2008.52","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a feature extraction method called two dimension locally principal component analysis (2DLPCA) for face recognition, which is based directly image matrix rather than 1D image vectors. 2DLPCA seeks to discover the intrinsic image local structure. This local structure may contain useful information for discrimination. Experimental results on ORL face database show the effectiveness of the proposed algorithm.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Chinese Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCPR.2008.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we propose a feature extraction method called two dimension locally principal component analysis (2DLPCA) for face recognition, which is based directly image matrix rather than 1D image vectors. 2DLPCA seeks to discover the intrinsic image local structure. This local structure may contain useful information for discrimination. Experimental results on ORL face database show the effectiveness of the proposed algorithm.
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