Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification

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Title Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification
Author Armand, Stephane; Blumenstein, Michael Myer; Muthukkumarasamy, Vallipuram
Publication Title 2006 International Joint Conference on Neural Networks
Editor Gary G. Yen
Year Published 2006
Place of publication USA
Publisher IEEE
Abstract Signatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signature's contour. A novel combination of the Modified Direction Feature (MDF) and additional distinguishing features such as the centroid, surface area, length and skew are used for classification. A Resilient Backpropagation (RBP) neural network and a Radial Basis Function (RBF) network were compared in terms of verification accuracy. Using a publicly available database of 2106 signatures (936 genuine and 1170 forgeries), verification rates of 91.21% and 88.0% were obtained using RBP and RBF respectively.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/servlet/opac?punumber=11216
Alternative URI http://dx.doi.org/10.1109/IJCNN.2006.246750
Copyright Statement Copyright 2006 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.
ISBN 0-7803-94909
Conference name 2006 International Joint Conference on Neural Networks
Location Vancouver, Canada
Date From 2006-07-16
Date To 2006-07-21
URI http://hdl.handle.net/10072/11883
Date Accessioned 2007-03-15
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Pattern Recognition
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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