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dc.contributor.convenorJosep Lladós
dc.contributor.authorNguyen, Vu
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorLeedham, Graham
dc.contributor.editorBob Werner
dc.date.accessioned2017-05-03T13:03:27Z
dc.date.available2017-05-03T13:03:27Z
dc.date.issued2009
dc.date.modified2010-06-03T09:03:08Z
dc.identifier.refurihttp://www.icdar2009.org
dc.identifier.doi10.1109/ICDAR.2009.123
dc.identifier.urihttp://hdl.handle.net/10072/29990
dc.description.abstractGlobal features based on the boundary of a signature and its projections are described for enhancing the process of automated signature verification. The first global feature is derived from the total 'energy' a writer uses to create their signature. The second feature employs information from the vertical and horizontal projections of a signature, focusing on the proportion of the distance between key strokes in the image, and the height/width of the signature. The combination of these features with the Modified Direction Feature (MDF) and the ratio feature showed promising results for the off-line signature verification problem. When being trained using 12 genuine specimens and 400 random forgeries taken from a publicly available database, the Support Vector Machine (SVM) classifier obtained an average error rate (AER) of 17.25%. The false acceptance rate (FAR) for random forgeries was also kept as low as 0.08%.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent804812 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.publisher.placeLos Alamitos, CA
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename10th International Conference on Document Analysis and Recognition
dc.relation.ispartofconferencetitleProceedings of the 10th International Conference on Document Analysis annd Recognition
dc.relation.ispartofdatefrom2009-07-26
dc.relation.ispartofdateto2009-07-29
dc.relation.ispartoflocationBarcelona
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computation
dc.subject.fieldofresearchcode080109
dc.subject.fieldofresearchcode080108
dc.titleGlobal Features for the Off-Line Signature Verification Problem
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2009 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.
gro.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorNguyen, Vu


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