Offline signature verification based on improved extracted features using neural network

The verification of handwritten signatures is one of the oldest and the most popular biometric authentication methods in our society. A history which spans several hundred years has ensured that it also has a wide legal acceptance all around the world. As technology improved, the different ways of c...

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Main Author:	Hussein, Karrar Neamah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/48546/1/KarrarNeamahHusseinMFC2014.pdf
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id	my-utm-ep.48546
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spelling	my-utm-ep.485462017-08-02T04:55:50Z Offline signature verification based on improved extracted features using neural network 2014 Hussein, Karrar Neamah TK Electrical engineering. Electronics Nuclear engineering The verification of handwritten signatures is one of the oldest and the most popular biometric authentication methods in our society. A history which spans several hundred years has ensured that it also has a wide legal acceptance all around the world. As technology improved, the different ways of comparing and analyzing signatures became more and more sophisticated. Since the early seventies, people have been exploring how computers may aid and maybe one day fully take over the task of signature verification. Based on the acquisition process, the field is divided into on-line and off-line parts. In on-line signature verification, the whole process of signing is captured using some kind of an acquisition device, while the off-line approach relies merely on the scanned images of signatures. This thesis addresses some of the many open questions in the off-line field. In this thesis, we present off line signature recognition and verification system which is based on image processing, New improved method for features extraction is proposed and artificial neural network are both used to attend the objective designed for this thesis, Two separate sequential neural networks are designed, one for signature recognition, and another for verification (i.e. for detecting forgery). Verification network parameters which are produced individually for every signature are controlled by a recognition network. The System overall performs is enough to signature recognition and verification signature standard and popular dataset, In order to demonstrate the practical applications of the results, a complete signature verification framework has been developed, Which incorporates all the previously introduced algorithms. The result was very good comparing with other work, sensitivity was more than 0.94% and 0.80% for training and testing data respectively, and for specificity it was more than 0.78% and 0.74 for training and testing data respectively, and for specificity. The results provided in this thesis aim to present a deeper analytical insight into the behavior of the verification system. 2014 Thesis http://eprints.utm.my/id/eprint/48546/ http://eprints.utm.my/id/eprint/48546/1/KarrarNeamahHusseinMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:80584?queryType=vitalDismax&query=Offline+signature+verification+based+on+improved+extracted+features+using+neural+network&public=true masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Hussein, Karrar Neamah Offline signature verification based on improved extracted features using neural network
description	The verification of handwritten signatures is one of the oldest and the most popular biometric authentication methods in our society. A history which spans several hundred years has ensured that it also has a wide legal acceptance all around the world. As technology improved, the different ways of comparing and analyzing signatures became more and more sophisticated. Since the early seventies, people have been exploring how computers may aid and maybe one day fully take over the task of signature verification. Based on the acquisition process, the field is divided into on-line and off-line parts. In on-line signature verification, the whole process of signing is captured using some kind of an acquisition device, while the off-line approach relies merely on the scanned images of signatures. This thesis addresses some of the many open questions in the off-line field. In this thesis, we present off line signature recognition and verification system which is based on image processing, New improved method for features extraction is proposed and artificial neural network are both used to attend the objective designed for this thesis, Two separate sequential neural networks are designed, one for signature recognition, and another for verification (i.e. for detecting forgery). Verification network parameters which are produced individually for every signature are controlled by a recognition network. The System overall performs is enough to signature recognition and verification signature standard and popular dataset, In order to demonstrate the practical applications of the results, a complete signature verification framework has been developed, Which incorporates all the previously introduced algorithms. The result was very good comparing with other work, sensitivity was more than 0.94% and 0.80% for training and testing data respectively, and for specificity it was more than 0.78% and 0.74 for training and testing data respectively, and for specificity. The results provided in this thesis aim to present a deeper analytical insight into the behavior of the verification system.
format	Thesis
qualification_level	Master's degree
author	Hussein, Karrar Neamah
author_facet	Hussein, Karrar Neamah
author_sort	Hussein, Karrar Neamah
title	Offline signature verification based on improved extracted features using neural network
title_short	Offline signature verification based on improved extracted features using neural network
title_full	Offline signature verification based on improved extracted features using neural network
title_fullStr	Offline signature verification based on improved extracted features using neural network
title_full_unstemmed	Offline signature verification based on improved extracted features using neural network
title_sort	offline signature verification based on improved extracted features using neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48546/1/KarrarNeamahHusseinMFC2014.pdf
_version_	1747817416812920832

Offline signature verification based on improved extracted features using neural network

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