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dc.contributor.authorLarkins, Robert L.en_NZ
dc.date.accessioned2009-03-10T12:17:01Z
dc.date.available2009-07-30T09:37:28Z
dc.date.issued2009en_NZ
dc.identifier.citationLarkins, R. L. (2009). Off-line signature verification (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2803en
dc.identifier.urihttps://hdl.handle.net/10289/2803
dc.description.abstractIn today’s society signatures are the most accepted form of identity verification. However, they have the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. This thesis implements and tests current approaches to off-line signature verification with the goal of determining the most beneficial techniques that are available. This investigation will also introduce novel techniques that are shown to significantly boost the achieved classification accuracy for both person-dependent (one-class training) and person-independent (two-class training) signature verification learning strategies. The findings presented in this thesis show that many common techniques do not always give any significant advantage and in some cases they actually detract from the classification accuracy. Using the techniques that are proven to be most beneficial, an effective approach to signature verification is constructed, which achieves approximately 90% and 91% on the standard CEDAR and GPDS signature datasets respectively. These results are significantly better than the majority of results that have been previously published. Additionally, this approach is shown to remain relatively stable when a minimal number of training signatures are used, representing feasibility for real-world situations.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectoff-line signature verificationen_NZ
dc.subjectsignature verificationen_NZ
dc.subjectadaptive feature thresholdingen_NZ
dc.titleOff-line signature verificationen_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineSchool of Computer and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2009-03-10T12:17:01Zen_NZ
uow.date.available2009-07-30T09:37:28Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20090310.121701en_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ


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