Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines

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Title Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines
Author Nguyen, Vu Minh; Blumenstein, Michael Myer; Muthukkumarasamy, Vallipuram; Leedham, Graham
Publication Title Proceedings : Ninth International Conference on Document Analysis and Recognition
Editor International Association for Pattern Recognition, TC10 and TC11
Year Published 2007
Publisher IEEE Computer Society
Abstract As a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signature's contour using the modified direction feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available database of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.ieee.org/portal/site
Alternative URI http://www.icdar2007.org/
Copyright Statement Copyright [year] 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 0769528228
Conference name ICDAR 2007 9th International Conference on Document Analysis and Recognition
Location Curitiba, Parana, Brazil
Date From 2007-09-23
Date To 2007-09-26
URI http://hdl.handle.net/10072/17596
Date Accessioned 2008-03-26
Date Available 2008-05-26T02:07:16Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Neural Networks, Genetic Alogrithms and Fuzzy Logic
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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