Off-line signature verification and forgery detection using fuzzy modeling
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 appr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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2005
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| Online Access: | http://shdl.mmu.edu.my/2254/ http://shdl.mmu.edu.my/2254/1/1558.pdf |
| _version_ | 1848790006223601664 |
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| author | Hanmandlu, , M Yusof, , MHM Madasu,, VK |
| author_facet | Hanmandlu, , M Yusof, , MHM Madasu,, VK |
| author_sort | Hanmandlu, , M |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:05:45Z |
| format | Article |
| id | mmu-2254 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:05:45Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-22542011-09-12T02:29:30Z http://shdl.mmu.edu.my/2254/ Off-line signature verification and forgery detection using fuzzy modeling Hanmandlu, , M Yusof, , MHM Madasu,, VK QA75.5-76.95 Electronic computers. Computer science 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. 2005-03 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2254/1/1558.pdf Hanmandlu, , M and Yusof, , MHM and Madasu,, VK (2005) Off-line signature verification and forgery detection using fuzzy modeling. Pattern Recognition, 38 (3). pp. 341-356. ISSN 00313203 http://dx.doi.org/10.1016/j.patcog.2004.05.015 doi:10.1016/j.patcog.2004.05.015 doi:10.1016/j.patcog.2004.05.015 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Hanmandlu, , M Yusof, , MHM Madasu,, VK Off-line signature verification and forgery detection using fuzzy modeling |
| title | Off-line signature verification and forgery detection using fuzzy modeling |
| title_full | Off-line signature verification and forgery detection using fuzzy modeling |
| title_fullStr | Off-line signature verification and forgery detection using fuzzy modeling |
| title_full_unstemmed | Off-line signature verification and forgery detection using fuzzy modeling |
| title_short | Off-line signature verification and forgery detection using fuzzy modeling |
| title_sort | off-line signature verification and forgery detection using fuzzy modeling |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2254/ http://shdl.mmu.edu.my/2254/ http://shdl.mmu.edu.my/2254/ http://shdl.mmu.edu.my/2254/1/1558.pdf |