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Signature verification system based on multiple classifiers and multi fusion decision approach

With an increase in identity fraud and the emphasis on security, there is growing and urgent need to verify human identify efficiently. Signature and the handwriting verification application are used in many fields such as banking, public sectors. Documents and cheques verification system has trigge...

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Main Author: Mokayed, Hamam M. Ibrahim
Format: Thesis
Language:English
Published: 2010
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/11302/6/HamamM.IbrahimMFKE2010.pdf
http://eprints.utm.my/id/eprint/11302/
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spelling my.utm.113022017-09-18T07:24:45Z http://eprints.utm.my/id/eprint/11302/ Signature verification system based on multiple classifiers and multi fusion decision approach Mokayed, Hamam M. Ibrahim TK Electrical engineering. Electronics Nuclear engineering With an increase in identity fraud and the emphasis on security, there is growing and urgent need to verify human identify efficiently. Signature and the handwriting verification application are used in many fields such as banking, public sectors. Documents and cheques verification system has triggered a real need for reliable, accurate and robust system. This work adopts different classification techniques between the local features based and the global features based of the signature system in addition to different fusion techniques between the outputs of the different classifiers and global features based to improve error rate of behavioral system. Main goal is to develop more accurate and robust signature verification system than the previous developed system with False Rejection Rate (FRR) equals to 5.3 and False Acceptance Rate (FAR) equals to 0. To achieve this goal, first multiple classification techniques are applied to the signature verification system which are artificial neural network, support vector machine and Pearson correlation and then these techniques are fused by applying two complicated fusion techniques which are fuzzy logic and sequential fuzzy logic and one simple fusion technique which is max voting. Lastly the rule-based decision is applied to specify whether the signature is genuine or not. Second, the improved signature verification system is extended with the high performance Hitachi system. This biometric based system can be realized in many real world and web based applications where there is a need for higher security and robust identification. 2010-01 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/11302/6/HamamM.IbrahimMFKE2010.pdf Mokayed, Hamam M. Ibrahim (2010) Signature verification system based on multiple classifiers and multi fusion decision approach. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mokayed, Hamam M. Ibrahim
Signature verification system based on multiple classifiers and multi fusion decision approach
description With an increase in identity fraud and the emphasis on security, there is growing and urgent need to verify human identify efficiently. Signature and the handwriting verification application are used in many fields such as banking, public sectors. Documents and cheques verification system has triggered a real need for reliable, accurate and robust system. This work adopts different classification techniques between the local features based and the global features based of the signature system in addition to different fusion techniques between the outputs of the different classifiers and global features based to improve error rate of behavioral system. Main goal is to develop more accurate and robust signature verification system than the previous developed system with False Rejection Rate (FRR) equals to 5.3 and False Acceptance Rate (FAR) equals to 0. To achieve this goal, first multiple classification techniques are applied to the signature verification system which are artificial neural network, support vector machine and Pearson correlation and then these techniques are fused by applying two complicated fusion techniques which are fuzzy logic and sequential fuzzy logic and one simple fusion technique which is max voting. Lastly the rule-based decision is applied to specify whether the signature is genuine or not. Second, the improved signature verification system is extended with the high performance Hitachi system. This biometric based system can be realized in many real world and web based applications where there is a need for higher security and robust identification.
format Thesis
author Mokayed, Hamam M. Ibrahim
author_facet Mokayed, Hamam M. Ibrahim
author_sort Mokayed, Hamam M. Ibrahim
title Signature verification system based on multiple classifiers and multi fusion decision approach
title_short Signature verification system based on multiple classifiers and multi fusion decision approach
title_full Signature verification system based on multiple classifiers and multi fusion decision approach
title_fullStr Signature verification system based on multiple classifiers and multi fusion decision approach
title_full_unstemmed Signature verification system based on multiple classifiers and multi fusion decision approach
title_sort signature verification system based on multiple classifiers and multi fusion decision approach
publishDate 2010
url http://eprints.utm.my/id/eprint/11302/6/HamamM.IbrahimMFKE2010.pdf
http://eprints.utm.my/id/eprint/11302/
_version_ 1643645643767414784
score 13.211869

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