An Efficient Human Identification through MultiModal Biometric System

Detalhes bibliográficos
Autor(a) principal: Meena,K.
Data de Publicação: 2016
Outros Autores: Malarvizhi,N.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Archives of Biology and Technology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300403
Resumo: ABSTRACT Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.
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spelling An Efficient Human Identification through MultiModal Biometric SystemMultimodal BiometricsFaceFinger printIrisContourlet transform;ClassificationSupport Vector MachineNN classifierABSTRACT Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.Instituto de Tecnologia do Paraná - Tecpar2016-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300403Brazilian Archives of Biology and Technology v.59 n.spe2 2016reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2016161055info:eu-repo/semantics/openAccessMeena,K.Malarvizhi,N.eng2017-01-19T00:00:00Zoai:scielo:S1516-89132016000300403Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2017-01-19T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false
dc.title.none.fl_str_mv An Efficient Human Identification through MultiModal Biometric System
title An Efficient Human Identification through MultiModal Biometric System
spellingShingle An Efficient Human Identification through MultiModal Biometric System
Meena,K.
Multimodal Biometrics
Face
Finger print
Iris
Contourlet transform;Classification
Support Vector Machine
NN classifier
title_short An Efficient Human Identification through MultiModal Biometric System
title_full An Efficient Human Identification through MultiModal Biometric System
title_fullStr An Efficient Human Identification through MultiModal Biometric System
title_full_unstemmed An Efficient Human Identification through MultiModal Biometric System
title_sort An Efficient Human Identification through MultiModal Biometric System
author Meena,K.
author_facet Meena,K.
Malarvizhi,N.
author_role author
author2 Malarvizhi,N.
author2_role author
dc.contributor.author.fl_str_mv Meena,K.
Malarvizhi,N.
dc.subject.por.fl_str_mv Multimodal Biometrics
Face
Finger print
Iris
Contourlet transform;Classification
Support Vector Machine
NN classifier
topic Multimodal Biometrics
Face
Finger print
Iris
Contourlet transform;Classification
Support Vector Machine
NN classifier
description ABSTRACT Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300403
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300403
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-4324-2016161055
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto de Tecnologia do Paraná - Tecpar
publisher.none.fl_str_mv Instituto de Tecnologia do Paraná - Tecpar
dc.source.none.fl_str_mv Brazilian Archives of Biology and Technology v.59 n.spe2 2016
reponame:Brazilian Archives of Biology and Technology
instname:Instituto de Tecnologia do Paraná (Tecpar)
instacron:TECPAR
instname_str Instituto de Tecnologia do Paraná (Tecpar)
instacron_str TECPAR
institution TECPAR
reponame_str Brazilian Archives of Biology and Technology
collection Brazilian Archives of Biology and Technology
repository.name.fl_str_mv Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)
repository.mail.fl_str_mv babt@tecpar.br||babt@tecpar.br
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