Global Features for the Off-Line Signature Verification Problem

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Title Global Features for the Off-Line Signature Verification Problem
Author Nguyen, Vu Minh; Blumenstein, Michael Myer; Leedham, Graham
Publication Title Proceedings of the 10th International Conference on Document Analysis annd Recognition
Editor Bob Werner
Year Published 2009
Place of publication Los Alamitos, CA
Publisher IEEE Computer Society
Abstract Global features based on the boundary of a signature and its projections are described for enhancing the process of automated signature verification. The first global feature is derived from the total 'energy' a writer uses to create their signature. The second feature employs information from the vertical and horizontal projections of a signature, focusing on the proportion of the distance between key strokes in the image, and the height/width of the signature. The combination of these features with the Modified Direction Feature (MDF) and the ratio feature showed promising results for the off-line signature verification problem. When being trained using 12 genuine specimens and 400 random forgeries taken from a publicly available database, the Support Vector Machine (SVM) classifier obtained an average error rate (AER) of 17.25%. The false acceptance rate (FAR) for random forgeries was also kept as low as 0.08%.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2009 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 978-0-7695-3725-2
Conference name 10th International Conference on Document Analysis and Recognition
Location Barcelona
Date From 2009-07-26
Date To 2009-07-29
Date Accessioned 2010-03-04
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Neural, Evolutionary and Fuzzy Computation; Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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