Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1248
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dc.contributorDepartment of Computing-
dc.creatorYang, J-
dc.creatorZhang, DD-
dc.creatorYang, JY-
dc.date.accessioned2014-12-11T08:26:17Z-
dc.date.available2014-12-11T08:26:17Z-
dc.identifier.isbn1424403421-
dc.identifier.urihttp://hdl.handle.net/10397/1248-
dc.language.isoenen_US
dc.publisherIEEEen_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.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.subjectFeature extractionen_US
dc.subjectManifold learningen_US
dc.subjectSubspace learningen_US
dc.subjectBiometricsen_US
dc.subjectPalmprint recognitionen_US
dc.titleNon-locality preserving projection and its application to palmprint recognitionen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dcterms.abstractThis paper develops a "Non-locality" Preserving Projection (NLPP) technique for feature extraction. In contrast to the existing Locality Preserving Projection (LPP), a technique based on the characterization of the local scatter, NLPP is a method based on the characterization of the non-local scatter. Intuitively, NLPP should be more effective than LPP when the non-local information plan a dominant role in discrimination. NLPP is tested using the PolyU palmprint database and the experimental results show that NLPP outperforms PCA, LDA and LPP.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2006 9th International Conference on Control, Automation, Robotics, and Vision : 5-8 December 2006, Singapore, [p. 1-4]-
dcterms.issued2006-
dc.identifier.scopus2-s2.0-34547183331-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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