Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/229
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dc.contributorDepartment of Computing-
dc.creatorSong, F-
dc.creatorZhang, DD-
dc.creatorMei, D-
dc.creatorGuo, Z-
dc.date.accessioned2014-12-11T08:22:47Z-
dc.date.available2014-12-11T08:22:47Z-
dc.identifier.issn1083-4419-
dc.identifier.urihttp://hdl.handle.net/10397/229-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2007 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.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectLinear discriminant criterionen_US
dc.titleA multiple maximum scatter difference discriminant criterion for facial feature extractionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1599-
dc.identifier.epage1606-
dc.identifier.volume37-
dc.identifier.issue6-
dc.identifier.doi10.1109/TSMCB.2007.906579-
dcterms.abstractMaximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart—multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature- extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD outperforms state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Dec. 2007, v. 37, no. 6, p.1599-1606-
dcterms.isPartOfIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics-
dcterms.issued2007-12-
dc.identifier.isiWOS:000251110300013-
dc.identifier.scopus2-s2.0-36749069892-
dc.identifier.pmid18179076-
dc.identifier.rosgroupidr35632-
dc.description.ros2007-2008 > 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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