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dc.contributor.authorLarkins, Robert L.
dc.contributor.authorMayo, Michael
dc.coverage.spatialConference held at Christchurch, New Zealanden_NZ
dc.date.accessioned2009-05-21T02:10:01Z
dc.date.available2009-05-21T02:10:01Z
dc.date.issued2008
dc.identifier.citationLarkins, R. & Mayo, M. (2008). Adaptive feature thresholding for off-line signature verification. In Proceeding of 23rd International Conference Image and Vision Computing New Zealand 2008(IVCNZ 2008).en
dc.identifier.urihttps://hdl.handle.net/10289/2174
dc.description.abstractThis paper introduces Adaptive Feature Thresholding (AFT) which is a novel method of person-dependent off-line signature verification. AFT enhances how a simple image feature of a signature is converted to a binary feature vector by significantly improving its representation in relation to the training signatures. The similarity between signatures is then easily computed from their corresponding binary feature vectors. AFT was tested on the CEDAR and GPDS benchmark datasets, with classification using either a manual or an automatic variant. On the CEDAR dataset we achieved a classification accuracy of 92% for manual and 90% for automatic, while on the GPDS dataset we achieved over 87% and 85% respectively. For both datasets AFT is less complex and requires fewer images features than the existing state of the art methods, while achieving competitive results.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE Pressen_NZ
dc.relation.urihttp://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=4762060&arnumber=4762072&count=89&index=11en
dc.rightsThis article has been published in the Proceeding of 23rd International Conference Image and Vision Computing New Zealand 2008 (IVCNZ 2008). ©2008 IEEE.en
dc.subjectcomputer scienceen
dc.subjectoff-line signature verificationen
dc.subjectperson-dependenten
dc.subjectfeature thresholdingen
dc.subjectspatial pyramiden
dc.subjectMachine learning
dc.titleAdaptive feature thresholding for off-line signature verificationen
dc.typeConference Contributionen
dc.identifier.doi10.1109/IVCNZ.2008.4762072en
dc.relation.isPartOfImage and Vision Computing New Zealand, 23rd International Conferenceen_NZ
pubs.begin-page1en_NZ
pubs.elements-id18470
pubs.end-page6en_NZ
pubs.finish-date2008-11-28en_NZ
pubs.place-of-publicationdoi: 10.1109/IVCNZ.2008.4762072en_NZ
pubs.start-date2008-11-26en_NZ


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