Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1218
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorWu, X-
dc.creatorWang, K-
dc.creatorZhang, DD-
dc.date.accessioned2014-12-11T08:27:19Z-
dc.date.available2014-12-11T08:27:19Z-
dc.identifier.isbn0-7803-7508-4-
dc.identifier.urihttp://hdl.handle.net/10397/1218-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2002 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.subjectPalmprint recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectWavelet energy featureen_US
dc.titleWavelet based palmprint recognitionen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dcterms.abstractPalmprint is a new biometric method to recognize a person. The features in a palmprint include principal lines, wrinkles and ridges, etc. Line structure feature, which includes principal lines and wrinkles, is one of the most popular methods in palmprint recognition. However, the line structure feature does not contain the thickness and width information of principal lines and wrinkles, which are very important to discriminate palmprints. Ridges are not included in line structure feature either. So these methods cannot distinguish different palmprints with similar line structure. Furthermore, the line extraction is a difficult task. The fact that principal lines, wrinkles and ridges have different resolutions motivates us to analyze the palmprint using multi-resolution analysis method. A novel palmprint feature, named wavelet energy features, is defined employing wavelet, which is a powerful tool of multi-resolution analysis, in this paper. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in several directions at different wavelet decomposition level (scale), so its ability to discriminate palms is very strong. Easiness to compute is another virtue of WEF. The very high recognition rates obtained in experiments shows the effect of the proposed method.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation1st International Conference on Machine Learning and Cybernetics : November 4-5, 2002, Beijing, China : proceedings, v. 3, p. 1253-1257-
dcterms.issued2002-
dc.identifier.scopus2-s2.0-0036925761-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
Files in This Item:
File Description SizeFormat 
palmprint-recognition_02.pdf304.03 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

110
Last Week
2
Last month
Citations as of Mar 24, 2024

Downloads

52
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

57
Last Week
0
Last month
Citations as of Mar 29, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.