Off-line signature verification and forgery detection using fuzzy modeling

Hanmandlu, Madasu, Yusof, Mohd. Hafizuddin Mohd. and Madasu, Vamsi Krishna (2005) Off-line signature verification and forgery detection using fuzzy modeling. Pattern Recognition, 38 3: 341-356. doi:10.1016/j.patcog.2004.05.015


Author Hanmandlu, Madasu
Yusof, Mohd. Hafizuddin Mohd.
Madasu, Vamsi Krishna
Title Off-line signature verification and forgery detection using fuzzy modeling
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2005
Sub-type Article (original research)
DOI 10.1016/j.patcog.2004.05.015
Volume 38
Issue 3
Start page 341
End page 356
Total pages 16
Editor Robert S. Ledley
Place of publication United Kingdom
Publisher Pergamon-Elsevier Science Ltd
Collection year 2005
Language eng
Subject C1
280207 Pattern Recognition
700199 Computer software and services not elsewhere classified
Abstract Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Off-line Signature Verification
Forgery Detection
Structural Parameters
Fuzzy Logic
Ts Model
Bank Check Recognition
Recognition
System
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2006 Higher Education Research Data Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Wed, 15 Aug 2007, 07:33:12 EST