Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1248
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Yang, J | - |
dc.creator | Zhang, DD | - |
dc.creator | Yang, JY | - |
dc.date.accessioned | 2014-12-11T08:26:17Z | - |
dc.date.available | 2014-12-11T08:26:17Z | - |
dc.identifier.isbn | 1424403421 | - |
dc.identifier.uri | http://hdl.handle.net/10397/1248 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_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.rights | 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.subject | Feature extraction | en_US |
dc.subject | Manifold learning | en_US |
dc.subject | Subspace learning | en_US |
dc.subject | Biometrics | en_US |
dc.subject | Palmprint recognition | en_US |
dc.title | Non-locality preserving projection and its application to palmprint recognition | en_US |
dc.type | Conference Paper | en_US |
dc.description.otherinformation | Author name used in this publication: David Zhang | en_US |
dcterms.abstract | This 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2006 9th International Conference on Control, Automation, Robotics, and Vision : 5-8 December 2006, Singapore, [p. 1-4] | - |
dcterms.issued | 2006 | - |
dc.identifier.scopus | 2-s2.0-34547183331 | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
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preserving-projection_06.pdf | 340.79 kB | Adobe PDF | View/Open |
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