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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54360
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dc.contributor.authorStarovoitov, V.-
dc.contributor.authorAkhundjanov, U.-
dc.coverage.spatialМинскen_US
dc.date.accessioned2024-02-23T11:41:53Z-
dc.date.available2024-02-23T11:41:53Z-
dc.date.issued2023-
dc.identifier.citationStarovoitov, V. Writer-Dependent Approach to Off-line Signature Verification / V. Starovoitov, U. Akhundjanov // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 241–244.en_US
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/54360-
dc.description.abstractResults of a new approach to off-line signature verification are presented. The approach is writer-dependent. To verify a signature, only 15≥N≥5 genuine signatures of the person are used. The signature images are pre-processed and normalized into a contour representation. We then compute two new signature features: the distribution of LBP values and local curvature of contours in the binary signature image. For a signature submitted for analysis, N genuine signatures of this person are randomly selected and a one-class SVM classifier is developed. Accuracy of our approach in verification of all 2640 signatures from the public CEDAR database was 99.77%. All fake signatures were correctly recognized even with N=5 genuine signatures used to build the classifier.en_US
dc.language.isoenen_US
dc.publisherBSUen_US
dc.subjectматериалы конференцийen_US
dc.subjectsignatureen_US
dc.subjectoff-line verificationen_US
dc.subjectimage processingen_US
dc.subjectfeaturesen_US
dc.subjectclassifieren_US
dc.subjectone-class SVMen_US
dc.titleWriter-Dependent Approach to Off-line Signature Verificationen_US
dc.typeArticleen_US
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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