Staff View: Analysis of 'goat' within user population of an offline signature biometrics

Analysis of 'goat' within user population of an offline signature biometrics

Intra - user variability inherent in human handwritten signatures remains one of the main challenges for a robust biometrics signature based authentication system. The existence of a subset of users classified as 'goats' in the Doddington's menagerie whose signature samples are highly...

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Main Authors:	Sharifah M.S.A., Asma S., Masyura A.F., Rina M.A.
Other Authors:	36680916600
Format:	Conference Paper
Published:	2023
Subjects:	Doddignton's menagerie Hidden markov model (HMM) Z-Score analysis Hidden Markov models Information science Population statistics Signal processing Authentication systems Biometric systems Centre of gravity Computational approach False rejection rate Handwritten signatures Input sample Local feature Offline signatures Prime-focus Reference models Signature images System accuracy Z-score analysis Biometrics
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id	my.uniten.dspace-29625
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spelling	my.uniten.dspace-296252024-04-17T10:16:00Z Analysis of 'goat' within user population of an offline signature biometrics Sharifah M.S.A. Asma S. Masyura A.F. Rina M.A. 36680916600 24722081200 35193815200 24721188400 Doddignton's menagerie Hidden markov model (HMM) Z-Score analysis Hidden Markov models Information science Population statistics Signal processing Authentication systems Biometric systems Centre of gravity Computational approach Doddignton's menagerie False rejection rate Handwritten signatures Hidden markov model (HMM) Input sample Local feature Offline signatures Prime-focus Reference models Signature images System accuracy Z-score analysis Biometrics Intra - user variability inherent in human handwritten signatures remains one of the main challenges for a robust biometrics signature based authentication system. The existence of a subset of users classified as 'goats' in the Doddington's menagerie whose signature samples are highly inconsistent and often rejected by the biometrics system may degrade the system accuracy by contributing a large portion to the False Rejection Rate (FRR). However, little is known on the level of the intra user variability and percentage of the 'goats' in the overall user population, which in turns remains the prime focus of this paper. An HMM-based computational approach is used to build the reference model and verifY the authenticity of an input sample based on a series of a local feature extracted from signature images. Here, four different goat populations are identified for offline signature biometric system which is based on four different local features ( namely pixel density, centre of gravity, angle, and distance) and are analysed for their co-relationship. The overall analysis is carried out on Sigma database which is compiled to reflect the signatures of a target user population. � 2010 IEEE. Final 2023-12-28T07:17:47Z 2023-12-28T07:17:47Z 2010 Conference Paper 10.1109/ISSPA.2010.5605415 2-s2.0-78650276249 https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650276249&doi=10.1109%2fISSPA.2010.5605415&partnerID=40&md5=24f638ca9ca035f88774e99a8a3f6b08 https://irepository.uniten.edu.my/handle/123456789/29625 5605415 765 769 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	Doddignton's menagerie Hidden markov model (HMM) Z-Score analysis Hidden Markov models Information science Population statistics Signal processing Authentication systems Biometric systems Centre of gravity Computational approach Doddignton's menagerie False rejection rate Handwritten signatures Hidden markov model (HMM) Input sample Local feature Offline signatures Prime-focus Reference models Signature images System accuracy Z-score analysis Biometrics
spellingShingle	Doddignton's menagerie Hidden markov model (HMM) Z-Score analysis Hidden Markov models Information science Population statistics Signal processing Authentication systems Biometric systems Centre of gravity Computational approach Doddignton's menagerie False rejection rate Handwritten signatures Hidden markov model (HMM) Input sample Local feature Offline signatures Prime-focus Reference models Signature images System accuracy Z-score analysis Biometrics Sharifah M.S.A. Asma S. Masyura A.F. Rina M.A. Analysis of 'goat' within user population of an offline signature biometrics
description	Intra - user variability inherent in human handwritten signatures remains one of the main challenges for a robust biometrics signature based authentication system. The existence of a subset of users classified as 'goats' in the Doddington's menagerie whose signature samples are highly inconsistent and often rejected by the biometrics system may degrade the system accuracy by contributing a large portion to the False Rejection Rate (FRR). However, little is known on the level of the intra user variability and percentage of the 'goats' in the overall user population, which in turns remains the prime focus of this paper. An HMM-based computational approach is used to build the reference model and verifY the authenticity of an input sample based on a series of a local feature extracted from signature images. Here, four different goat populations are identified for offline signature biometric system which is based on four different local features ( namely pixel density, centre of gravity, angle, and distance) and are analysed for their co-relationship. The overall analysis is carried out on Sigma database which is compiled to reflect the signatures of a target user population. � 2010 IEEE.
author2	36680916600
author_facet	36680916600 Sharifah M.S.A. Asma S. Masyura A.F. Rina M.A.
format	Conference Paper
author	Sharifah M.S.A. Asma S. Masyura A.F. Rina M.A.
author_sort	Sharifah M.S.A.
title	Analysis of 'goat' within user population of an offline signature biometrics
title_short	Analysis of 'goat' within user population of an offline signature biometrics
title_full	Analysis of 'goat' within user population of an offline signature biometrics
title_fullStr	Analysis of 'goat' within user population of an offline signature biometrics
title_full_unstemmed	Analysis of 'goat' within user population of an offline signature biometrics
title_sort	analysis of 'goat' within user population of an offline signature biometrics
publishDate	2023
_version_	1806426116384096256
score	13.222552

Analysis of 'goat' within user population of an offline signature biometrics

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