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Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems

This paper presents a study on the performance of different features in distinguishing between genuine and forged signatures for HMM based online and offline signature verification systems. The online features considered in the study include speed, angle along the trajectory, pen pressure and accele...

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Main Authors:	Shakil A., Ahmad S.M.S., Anwar R.B.M., Balbed M.A.M.
Other Authors:	24722081200
Format:	Conference Paper
Published:	2023
Subjects:	Hidden Markov models Pixels Analysis techniques Centre of gravity Equal error rate Hidden Markov modeling Off-line signature verification Offline Pen-pressure Signature verification Skilled forgery Online systems
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id	my.uniten.dspace-29711
record_format	dspace
spelling	my.uniten.dspace-297112024-04-17T10:24:41Z Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems Shakil A. Ahmad S.M.S. Anwar R.B.M. Balbed M.A.M. 24722081200 24721182400 24721188400 24721384800 Hidden Markov models Pixels Analysis techniques Centre of gravity Equal error rate Hidden Markov modeling Off-line signature verification Offline Pen-pressure Signature verification Skilled forgery Online systems This paper presents a study on the performance of different features in distinguishing between genuine and forged signatures for HMM based online and offline signature verification systems. The online features considered in the study include speed, angle along the trajectory, pen pressure and acceleration. The offline features include pixel density, centre of gravity, distance and angle. All features considered are local in nature Two analysis techniques are considered - ANOVA based and Equal Error rate (EER) based. Experimental results show that all online features have a high distinguishing capability while for the offline case, angle and distance are good for distinguishing between genuine and skilled forgeries for an HMM based signature verification system while pixel density and centre of gravity are not. � 2008 IEEE. Final 2023-12-28T07:41:45Z 2023-12-28T07:41:45Z 2008 Conference Paper 10.1109/DICTA.2008.76 2-s2.0-67549137657 https://www.scopus.com/inward/record.uri?eid=2-s2.0-67549137657&doi=10.1109%2fDICTA.2008.76&partnerID=40&md5=715eda36383c1fcfeee8c3b669f4e25c https://irepository.uniten.edu.my/handle/123456789/29711 4700073 572 577 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Hidden Markov models Pixels Analysis techniques Centre of gravity Equal error rate Hidden Markov modeling Off-line signature verification Offline Pen-pressure Signature verification Skilled forgery Online systems
spellingShingle	Hidden Markov models Pixels Analysis techniques Centre of gravity Equal error rate Hidden Markov modeling Off-line signature verification Offline Pen-pressure Signature verification Skilled forgery Online systems Shakil A. Ahmad S.M.S. Anwar R.B.M. Balbed M.A.M. Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
description	This paper presents a study on the performance of different features in distinguishing between genuine and forged signatures for HMM based online and offline signature verification systems. The online features considered in the study include speed, angle along the trajectory, pen pressure and acceleration. The offline features include pixel density, centre of gravity, distance and angle. All features considered are local in nature Two analysis techniques are considered - ANOVA based and Equal Error rate (EER) based. Experimental results show that all online features have a high distinguishing capability while for the offline case, angle and distance are good for distinguishing between genuine and skilled forgeries for an HMM based signature verification system while pixel density and centre of gravity are not. � 2008 IEEE.
author2	24722081200
author_facet	24722081200 Shakil A. Ahmad S.M.S. Anwar R.B.M. Balbed M.A.M.
format	Conference Paper
author	Shakil A. Ahmad S.M.S. Anwar R.B.M. Balbed M.A.M.
author_sort	Shakil A.
title	Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
title_short	Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
title_full	Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
title_fullStr	Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
title_full_unstemmed	Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
title_sort	analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems
publishDate	2023
_version_	1806426572643631104
score	13.222552

Analysis of the effect of different features' performance on hidden markov modeling based online and offline signature verification systems

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