Multi-source deep transfer learning for cross-sensor biometrics
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/6076 http://dx.doi.org/10.1007/s00521-016-2325-5 |
Resumo: | Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios. |
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7160 |
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Multi-source deep transfer learning for cross-sensor biometricsDeep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios.2018-01-14T20:45:36Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6076http://dx.doi.org/10.1007/s00521-016-2325-5engChetak KandaswamyMonteiro,JCSilva,LMJaime Cardosoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:40Zoai:repositorio.inesctec.pt:123456789/6076Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:27.614742Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Multi-source deep transfer learning for cross-sensor biometrics |
title |
Multi-source deep transfer learning for cross-sensor biometrics |
spellingShingle |
Multi-source deep transfer learning for cross-sensor biometrics Chetak Kandaswamy |
title_short |
Multi-source deep transfer learning for cross-sensor biometrics |
title_full |
Multi-source deep transfer learning for cross-sensor biometrics |
title_fullStr |
Multi-source deep transfer learning for cross-sensor biometrics |
title_full_unstemmed |
Multi-source deep transfer learning for cross-sensor biometrics |
title_sort |
Multi-source deep transfer learning for cross-sensor biometrics |
author |
Chetak Kandaswamy |
author_facet |
Chetak Kandaswamy Monteiro,JC Silva,LM Jaime Cardoso |
author_role |
author |
author2 |
Monteiro,JC Silva,LM Jaime Cardoso |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Chetak Kandaswamy Monteiro,JC Silva,LM Jaime Cardoso |
description |
Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-14T20:45:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/6076 http://dx.doi.org/10.1007/s00521-016-2325-5 |
url |
http://repositorio.inesctec.pt/handle/123456789/6076 http://dx.doi.org/10.1007/s00521-016-2325-5 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131609046188032 |