Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/235
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dc.contributorDepartment of Computing-
dc.creatorZhang, L-
dc.creatorZhang, DD-
dc.date.accessioned2014-12-11T08:22:48Z-
dc.date.available2014-12-11T08:22:48Z-
dc.identifier.issn1083-4419-
dc.identifier.urihttp://hdl.handle.net/10397/235-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_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.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.subjectBiometricsen_US
dc.subjectContext modelingen_US
dc.subjectFeature extractionen_US
dc.subjectPalmprints indentificationen_US
dc.subjectWavelet transformen_US
dc.titleCharacterization of palmprints by wavelet signatures via directional context modelingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1335-
dc.identifier.epage1347-
dc.identifier.volume34-
dc.identifier.issue3-
dc.identifier.doi10.1109/TSMCB.2004.824521-
dcterms.abstractThe palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting- points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, June 2004, v. 34, no. 3, p.1335-1347-
dcterms.isPartOfIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics-
dcterms.issued2004-06-
dc.identifier.isiWOS:000221578100003-
dc.identifier.scopus2-s2.0-2942549049-
dc.identifier.pmid15484907-
dc.identifier.rosgroupidr19420-
dc.description.ros2003-2004 > 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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