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dc.contributorDepartment of Computing-
dc.creatorLu, G-
dc.creatorWang, K-
dc.creatorZhang, DD-
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.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.subjectIndependent Component Analysisen_US
dc.subjectPalmprint identificationen_US
dc.subjectMulti-resolution analysisen_US
dc.titleWavelet based independent component analysis for palmprint identificationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dcterms.abstractThis 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.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the third International Conference on Machine Learning and Cybernetics : August 26-29, 2004, Shanghai, China, v. 6, p. 3547-3550-
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