Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/190
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorYang, J-
dc.creatorZhang, DD-
dc.creatorFrangi, AF-
dc.creatorYang, JY-
dc.date.accessioned2014-12-11T08:27:09Z-
dc.date.available2014-12-11T08:27:09Z-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10397/190-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.subjectEigenfacesen_US
dc.subjectFeature extractionen_US
dc.subjectImage representationen_US
dc.subjectFace recognitionen_US
dc.titleTwo-dimensional PCA : a new approach to appearance-based face representation and recognitionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage131-
dc.identifier.epage137-
dc.identifier.volume26-
dc.identifier.issue1-
dc.identifier.doi10.1109/TPAMI.2004.1261097-
dcterms.abstractIn this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.-
dcterms.bibliographicCitationIEEE transactions on pattern analysis and machine intelligence, Jan. 2004, v. 26, no. 1, p. 131-137-
dcterms.isPartOfIEEE transactions on pattern analysis and machine intelligence-
dcterms.issued2004-01-
dc.identifier.isiWOS:000187161400012-
dc.identifier.scopus2-s2.0-0742268833-
dc.identifier.pmid15382693-
dc.identifier.eissn1939-3539-
dc.identifier.rosgroupidr20220-
dc.description.ros2003-2004 > Academic research: refereed > Publication in refereed journal-
dc.description.oapublished_final-
Appears in Collections:Journal/Magazine Article
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