A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2012.6252660 http://hdl.handle.net/11449/42141 |
Resumo: | Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier. |
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A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case StudyMajority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.São Paulo State Univ, Fac Sci, Dept Comp, São Paulo, BrazilSão Paulo State Univ, Fac Sci, Dept Comp, São Paulo, BrazilIEEEUniversidade Estadual Paulista (Unesp)Afonso, Luis C. S. [UNESP]Papa, João Paulo [UNESP]Marana, Aparecido Nilceu [UNESP]Poursaberi, AhmadYanushkevich, Svetlana N.2014-05-20T15:33:33Z2014-05-20T15:33:33Z2012-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject5http://dx.doi.org/10.1109/IJCNN.2012.62526602012 International Joint Conference on Neural Networks (ijcnn). New York: IEEE, p. 5, 2012.1098-7576http://hdl.handle.net/11449/4214110.1109/IJCNN.2012.6252660WOS:0003093413020192-s2.0-8486507648790391829327471946027713750942689Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2012 International Joint Conference on Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/42141Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
title |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
spellingShingle |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study Afonso, Luis C. S. [UNESP] |
title_short |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
title_full |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
title_fullStr |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
title_full_unstemmed |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
title_sort |
A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study |
author |
Afonso, Luis C. S. [UNESP] |
author_facet |
Afonso, Luis C. S. [UNESP] Papa, João Paulo [UNESP] Marana, Aparecido Nilceu [UNESP] Poursaberi, Ahmad Yanushkevich, Svetlana N. |
author_role |
author |
author2 |
Papa, João Paulo [UNESP] Marana, Aparecido Nilceu [UNESP] Poursaberi, Ahmad Yanushkevich, Svetlana N. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Afonso, Luis C. S. [UNESP] Papa, João Paulo [UNESP] Marana, Aparecido Nilceu [UNESP] Poursaberi, Ahmad Yanushkevich, Svetlana N. |
description |
Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01 2014-05-20T15:33:33Z 2014-05-20T15:33:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2012.6252660 2012 International Joint Conference on Neural Networks (ijcnn). New York: IEEE, p. 5, 2012. 1098-7576 http://hdl.handle.net/11449/42141 10.1109/IJCNN.2012.6252660 WOS:000309341302019 2-s2.0-84865076487 9039182932747194 6027713750942689 |
url |
http://dx.doi.org/10.1109/IJCNN.2012.6252660 http://hdl.handle.net/11449/42141 |
identifier_str_mv |
2012 International Joint Conference on Neural Networks (ijcnn). New York: IEEE, p. 5, 2012. 1098-7576 10.1109/IJCNN.2012.6252660 WOS:000309341302019 2-s2.0-84865076487 9039182932747194 6027713750942689 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2012 International Joint Conference on Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
5 |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1797790176811941888 |