Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/241
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dc.contributorDepartment of Computing-
dc.creatorZuo, W-
dc.creatorZhang, DD-
dc.creatorWang, K-
dc.date.accessioned2014-12-11T08:27:51Z-
dc.date.available2014-12-11T08:27:51Z-
dc.identifier.issn1083-4419-
dc.identifier.urihttp://hdl.handle.net/10397/241-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2006 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. This 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.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectImage recognitionen_US
dc.subjectNearest feature lineen_US
dc.subjectPalmprint recognitionen_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.titleBidirectional PCA with assembled matrix distance metric for image recognitionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage863-
dc.identifier.epage872-
dc.identifier.volume36-
dc.identifier.issue4-
dc.identifier.doi10.1109/TSMCB.2006.872274-
dcterms.abstractPrincipal component analysis (PCA) has been very successful in image recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD)metric. For feature extraction, BD-PCA is proposed, which can be used for image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for image recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in image recognition.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Aug. 2006, v. 36, no. 4, p. 863-872-
dcterms.isPartOfIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics-
dcterms.issued2006-08-
dc.identifier.isiWOS:000239408100011-
dc.identifier.scopus2-s2.0-33746804077-
dc.identifier.pmid16903370-
dc.identifier.rosgroupidr32952-
dc.description.ros2006-2007 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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