Multiple classifier for on-line signature verification system

With the increase of advance development in security technology, many major corporations and governments start employing modern techniques to identify the identity of the individual. These include the adoption of a system such as on-line signature and handwriting verification application for banking...

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Main Author:	Esmaiel, Amjad Ali
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12684/1/AmjadAliEsmaielMFSKSM2009.pdf
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id	my-utm-ep.12684
record_format	uketd_dc
spelling	my-utm-ep.126842018-06-25T08:57:52Z Multiple classifier for on-line signature verification system 2009 Esmaiel, Amjad Ali TK Electrical engineering. Electronics Nuclear engineering With the increase of advance development in security technology, many major corporations and governments start employing modern techniques to identify the identity of the individual. These include the adoption of a system such as on-line signature and handwriting verification application for banking systems, public sectors, as well as for documents and checks. To achieve better solutions, multimodal biometric system needs to be employed since this system exploits more than one psychological or behavioral at verification process. This work presents a signature verification system as behavioral system to ensure that the currency authentication is preserved by validating the genuine signature. This study developed signatures by applying multiple classification techniques. These include Artificial Neural Network (ANN), Support Vector Machine (SVM) and pearson correlation. These techniques are combined with fusion techniques, i.e., ordinal structure module of fuzzy and Or gate to determine the signature either it is real or forge. The average of the values we have it after applying multiple classification techniques is calculated, and the results are compared with the pre-defined threshold prior to decision making of either the signature is genuine or not. After collect many samples and calculate the final result we calculate the error rate for FRR and FAR to compare it with previous study. After calculated the error rate we found 2% for False Rejection Rate (FRR) and 0% for False Acceptance Rate (FAR), so the result for these study it’s better than previous one. 2009 Thesis http://eprints.utm.my/id/eprint/12684/ http://eprints.utm.my/id/eprint/12684/1/AmjadAliEsmaielMFSKSM2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information Systems
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Esmaiel, Amjad Ali Multiple classifier for on-line signature verification system
description	With the increase of advance development in security technology, many major corporations and governments start employing modern techniques to identify the identity of the individual. These include the adoption of a system such as on-line signature and handwriting verification application for banking systems, public sectors, as well as for documents and checks. To achieve better solutions, multimodal biometric system needs to be employed since this system exploits more than one psychological or behavioral at verification process. This work presents a signature verification system as behavioral system to ensure that the currency authentication is preserved by validating the genuine signature. This study developed signatures by applying multiple classification techniques. These include Artificial Neural Network (ANN), Support Vector Machine (SVM) and pearson correlation. These techniques are combined with fusion techniques, i.e., ordinal structure module of fuzzy and Or gate to determine the signature either it is real or forge. The average of the values we have it after applying multiple classification techniques is calculated, and the results are compared with the pre-defined threshold prior to decision making of either the signature is genuine or not. After collect many samples and calculate the final result we calculate the error rate for FRR and FAR to compare it with previous study. After calculated the error rate we found 2% for False Rejection Rate (FRR) and 0% for False Acceptance Rate (FAR), so the result for these study it’s better than previous one.
format	Thesis
qualification_level	Master's degree
author	Esmaiel, Amjad Ali
author_facet	Esmaiel, Amjad Ali
author_sort	Esmaiel, Amjad Ali
title	Multiple classifier for on-line signature verification system
title_short	Multiple classifier for on-line signature verification system
title_full	Multiple classifier for on-line signature verification system
title_fullStr	Multiple classifier for on-line signature verification system
title_full_unstemmed	Multiple classifier for on-line signature verification system
title_sort	multiple classifier for on-line signature verification system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information Systems
publishDate	2009
url	http://eprints.utm.my/id/eprint/12684/1/AmjadAliEsmaielMFSKSM2009.pdf
_version_	1747814946013446144

Multiple classifier for on-line signature verification system

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