Show simple item record

dc.contributor.authorNguyen, Vuen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorMuthukkumarasamy, Vallipuramen_US
dc.contributor.authorLeedham, Grahamen_US
dc.contributor.editorInternational Association for Pattern Recognition, TC10 and TC11en_US
dc.date.accessioned2017-05-03T13:03:24Z
dc.date.available2017-05-03T13:03:24Z
dc.date.issued2007en_US
dc.date.modified2008-05-26T02:07:16Z
dc.identifier.refuriwww.icdar2007.orgen_AU
dc.identifier.doi10.1109/ICDAR.2007.4377012en_AU
dc.identifier.urihttp://hdl.handle.net/10072/17596
dc.description.abstractAs a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signature's contour using the modified direction feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available database of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent361742 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://www.ieee.org/portal/siteen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameICDAR 2007 9th International Conference on Document Analysis and Recognitionen_US
dc.relation.ispartofconferencetitleProceedings : Ninth International Conference on Document Analysis and Recognitionen_US
dc.relation.ispartofdatefrom2007-09-23en_US
dc.relation.ispartofdateto2007-09-26en_US
dc.relation.ispartoflocationCuritiba, Parana, Brazilen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280212en_US
dc.titleOff-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machinesen_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 [year] 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.issued2007
gro.hasfulltextFull Text


Files in this item

This item appears in the following Collection(s)

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

Show simple item record