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dc.contributor.convenorGary G. Yenen_AU
dc.contributor.authorArmand, Stephaneen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorMuthukkumarasamy, Vallipuramen_US
dc.contributor.editorGary G. Yenen_US
dc.date.accessioned2017-04-24T11:12:33Z
dc.date.available2017-04-24T11:12:33Z
dc.date.issued2006en_US
dc.date.modified2009-09-28T06:52:05Z
dc.identifier.refurihttp://www.wcci2006.orgen_AU
dc.identifier.doi10.1109/IJCNN.2006.246750en_AU
dc.identifier.urihttp://hdl.handle.net/10072/11883
dc.description.abstractSignatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signature's contour. A novel combination of the Modified Direction Feature (MDF) and additional distinguishing features such as the centroid, surface area, length and skew are used for classification. A Resilient Backpropagation (RBP) neural network and a Radial Basis Function (RBF) network were compared in terms of verification accuracy. Using a publicly available database of 2106 signatures (936 genuine and 1170 forgeries), verification rates of 91.21% and 88.0% were obtained using RBP and RBF respectively.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent27815 bytes
dc.format.extent616631 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://ieeexplore.ieee.org/servlet/opac?punumber=11216en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename2006 International Joint Conference on Neural Networksen_US
dc.relation.ispartofconferencetitle2006 International Joint Conference on Neural Networksen_US
dc.relation.ispartofdatefrom2006-07-16en_US
dc.relation.ispartofdateto2006-07-21en_US
dc.relation.ispartoflocationVancouver, Canadaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleOff-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classificationen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 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_AU
gro.date.issued2006
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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