Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1251
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Lu, G | - |
dc.creator | Wang, K | - |
dc.creator | Zhang, DD | - |
dc.date.accessioned | 2014-12-11T08:27:39Z | - |
dc.date.available | 2014-12-11T08:27:39Z | - |
dc.identifier.isbn | 0-7803-8403-2 | - |
dc.identifier.uri | http://hdl.handle.net/10397/1251 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_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.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 | Independent Component Analysis | en_US |
dc.subject | Palmprint identification | en_US |
dc.subject | Multi-resolution analysis | en_US |
dc.title | Wavelet based independent component analysis for palmprint identification | 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 presents a multi-resolution analysis based Independent Component Analysis (ICA) method for automatic palmprint identification. The ICA is well known by its feature representation ability recently, in which the desired representation is the one that minimizes the statistical independence of the components of the representation. Such a representation can capture the essential feature and the structure of the palmprint images. At the same time, the palmprints have a great deal of different features, such as principal lines, wrinkles, ridges, minutiae points and texture, which can be regarded as multi-scale features. Then, it is reasonable for us to integrate the multi-resolution analysis method and ICA to represent the palmprint features. The experiment results show that the integrated method is more efficient than ICA algorithm. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Proceedings of the third International Conference on Machine Learning and Cybernetics : August 26-29, 2004, Shanghai, China, v. 6, p. 3547-3550 | - |
dcterms.issued | 2004 | - |
dc.identifier.scopus | 2-s2.0-6344229781 | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | VoR allowed | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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wavelet-based-independent_04.pdf | 265.41 kB | Adobe PDF | View/Open |
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