Dorsal hand vein biometrics with a novel deep learning approach for person identification
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61948 |
Resumo: | Hand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system. |
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Acta scientiarum. Technology (Online) |
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Dorsal hand vein biometrics with a novel deep learning approach for person identificationDorsal hand vein biometrics with a novel deep learning approach for person identificationdorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regionsdorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regionsHand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system.Hand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system.Universidade Estadual De Maringá2023-04-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6194810.4025/actascitechnol.v45i1.61948Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e61948Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e619481806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61948/751375155831Copyright (c) 2023 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessBabalola, Felix Olanrewaju Bitirim , Yıltan Toygar, Önsen2023-05-25T13:56:55Zoai:periodicos.uem.br/ojs:article/61948Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2023-05-25T13:56:55Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Dorsal hand vein biometrics with a novel deep learning approach for person identification Dorsal hand vein biometrics with a novel deep learning approach for person identification |
title |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
spellingShingle |
Dorsal hand vein biometrics with a novel deep learning approach for person identification Babalola, Felix Olanrewaju dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions |
title_short |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
title_full |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
title_fullStr |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
title_full_unstemmed |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
title_sort |
Dorsal hand vein biometrics with a novel deep learning approach for person identification |
author |
Babalola, Felix Olanrewaju |
author_facet |
Babalola, Felix Olanrewaju Bitirim , Yıltan Toygar, Önsen |
author_role |
author |
author2 |
Bitirim , Yıltan Toygar, Önsen |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Babalola, Felix Olanrewaju Bitirim , Yıltan Toygar, Önsen |
dc.subject.por.fl_str_mv |
dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions |
topic |
dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions dorsal vein recognition; convolutional neural network; score-level fusion; overlapping image regions |
description |
Hand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-28 |
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 |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61948 10.4025/actascitechnol.v45i1.61948 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61948 |
identifier_str_mv |
10.4025/actascitechnol.v45i1.61948 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61948/751375155831 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e61948 Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e61948 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315338129571840 |