The Power of Ensemble Models in Fingerprint Classification: A case study
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557 |
Resumo: | The usage of fingerprints as biometrics has been practiced for more than 100 years, with the popularization of sensors and fingerprint capturing methodologies, the usage of this method for authentication and recognition has grown in the past years. However, the usage for recognition in large databases with a huge number of entries is computationally costly, hence the classification of fingerprints aims to attenuate this cost by increasing optimization. This paper presents a performance comparison between two ensemble of classifiers and a decision tree classifier, applied to the database from a known benchmark, the NIST sd-14 database, for the classification of fingerprints. The comparison performed using the stratified cross-validation process to set confidence interval for the evaluation of performance measured by the success rate, using a Random Forest, XGBoost and Decision Tree as classifiers. The one-tailed paired t-test showed that Random Forest and XGBoost don’t have statistical difference with significance of 95%, however, their performance is superior to the simple classifier Decision Tree. |
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INFOCOMP: Jornal de Ciência da Computação |
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The Power of Ensemble Models in Fingerprint Classification: A case studyThe usage of fingerprints as biometrics has been practiced for more than 100 years, with the popularization of sensors and fingerprint capturing methodologies, the usage of this method for authentication and recognition has grown in the past years. However, the usage for recognition in large databases with a huge number of entries is computationally costly, hence the classification of fingerprints aims to attenuate this cost by increasing optimization. This paper presents a performance comparison between two ensemble of classifiers and a decision tree classifier, applied to the database from a known benchmark, the NIST sd-14 database, for the classification of fingerprints. The comparison performed using the stratified cross-validation process to set confidence interval for the evaluation of performance measured by the success rate, using a Random Forest, XGBoost and Decision Tree as classifiers. The one-tailed paired t-test showed that Random Forest and XGBoost don’t have statistical difference with significance of 95%, however, their performance is superior to the simple classifier Decision Tree.Editora da UFLA2018-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557INFOCOMP Journal of Computer Science; Vol. 17 No. 1 (2018): June 2018; 1-101982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557/497Mendes, Raphael de LimaOliveira Neto, Rosalvo Ferreira deinfo:eu-repo/semantics/openAccess2018-07-23T21:56:34Zoai:infocomp.dcc.ufla.br:article/557Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:43.047991INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
The Power of Ensemble Models in Fingerprint Classification: A case study |
title |
The Power of Ensemble Models in Fingerprint Classification: A case study |
spellingShingle |
The Power of Ensemble Models in Fingerprint Classification: A case study Mendes, Raphael de Lima |
title_short |
The Power of Ensemble Models in Fingerprint Classification: A case study |
title_full |
The Power of Ensemble Models in Fingerprint Classification: A case study |
title_fullStr |
The Power of Ensemble Models in Fingerprint Classification: A case study |
title_full_unstemmed |
The Power of Ensemble Models in Fingerprint Classification: A case study |
title_sort |
The Power of Ensemble Models in Fingerprint Classification: A case study |
author |
Mendes, Raphael de Lima |
author_facet |
Mendes, Raphael de Lima Oliveira Neto, Rosalvo Ferreira de |
author_role |
author |
author2 |
Oliveira Neto, Rosalvo Ferreira de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Mendes, Raphael de Lima Oliveira Neto, Rosalvo Ferreira de |
description |
The usage of fingerprints as biometrics has been practiced for more than 100 years, with the popularization of sensors and fingerprint capturing methodologies, the usage of this method for authentication and recognition has grown in the past years. However, the usage for recognition in large databases with a huge number of entries is computationally costly, hence the classification of fingerprints aims to attenuate this cost by increasing optimization. This paper presents a performance comparison between two ensemble of classifiers and a decision tree classifier, applied to the database from a known benchmark, the NIST sd-14 database, for the classification of fingerprints. The comparison performed using the stratified cross-validation process to set confidence interval for the evaluation of performance measured by the success rate, using a Random Forest, XGBoost and Decision Tree as classifiers. The one-tailed paired t-test showed that Random Forest and XGBoost don’t have statistical difference with significance of 95%, however, their performance is superior to the simple classifier Decision Tree. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/557/497 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 17 No. 1 (2018): June 2018; 1-10 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874742170157056 |