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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/737

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contributor.authorJena, D-
contributor.authorMajhi, B-
contributor.authorPanigrahy, S K-
contributor.authorJena, S K-
date.accessioned2008-10-25T13:08:25Z-
date.available2008-10-25T13:08:25Z-
date.issued2008-08-16-
identifier.citation7th IEEE International Conference on Cognitive Informatics (ICCI 2008), 2008.en
identifier.isbn978-1-4244-2538-9-
identifier.otherDOI: 10.1109/COGINF.2008.4639204-
identifier.urihttp://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4639204-
identifier.urihttp://hdl.handle.net/2080/737-
descriptionCopyright for this article belongs to the publisheren
description.abstractIn this paper a novel offline signature verification scheme has been proposed. The scheme is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective of the work is to reduce the two vital parameters False Acceptance Rate (FAR) and False Rejection Rate (FRR) normally used in any signature verification scheme. In the end comparative analysis has been made with standard existing schemes.en
format.extent703499 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEE Xploreen
subjectOffline signatureen
subjectGeometric centreen
subjectFeature pointen
subjectForgeriesen
subjectEuclidean Distance Modelen
subjectFAR (False Acceptance Rate)en
subjectFRR (False Rejection Rate)en
titleImproved Offline Signature Verification Scheme Using Feature Point Extraction Methoden
typeArticleen
Appears in Collections:Conference Papers

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