3D face recognition with reconstructed faces from a collection of 2D images
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-13469-3_69 http://hdl.handle.net/11449/190202 |
Resumo: | Nowadays, there is an increasing need for systems that can accurately and quickly identify a person. Traditional identification methods utilize something a person knows or something a person has. This kind of methods has several drawbacks, being the main one the fact that it is impossible to detect an imposter who uses genuine credentials to pass as a genuine person. One way to solve these kinds of problems is to utilize biometric identification. The face is one of the biometric features that best suits the covert identification. However, in general, biometric systems based on 2D face recognition perform very poorly in unconstrained environments, common in covert identification scenarios, since the input images present variations in pose, illumination, and facial expressions. One way to mitigate this problem is to use 3D face data, but the current 3D scanners are expensive and require a lot of cooperation from people being identified. Therefore, in this work, we propose an approach based on local descriptors for 3D Face Recognition based on 3D face models reconstructed from collections of 2D images. Initial results show 95% in a subset of the LFW Face dataset. |
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Repositório Institucional da UNESP |
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3D face recognition with reconstructed faces from a collection of 2D images3D face recognition3DLBPBiometricsFace reconstructionNowadays, there is an increasing need for systems that can accurately and quickly identify a person. Traditional identification methods utilize something a person knows or something a person has. This kind of methods has several drawbacks, being the main one the fact that it is impossible to detect an imposter who uses genuine credentials to pass as a genuine person. One way to solve these kinds of problems is to utilize biometric identification. The face is one of the biometric features that best suits the covert identification. However, in general, biometric systems based on 2D face recognition perform very poorly in unconstrained environments, common in covert identification scenarios, since the input images present variations in pose, illumination, and facial expressions. One way to mitigate this problem is to use 3D face data, but the current 3D scanners are expensive and require a lot of cooperation from people being identified. Therefore, in this work, we propose an approach based on local descriptors for 3D Face Recognition based on 3D face models reconstructed from collections of 2D images. Initial results show 95% in a subset of the LFW Face dataset.São Carlos Federal University - UFSCARUNESP - São Paulo State UniversityUNESP - São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Neto, João Baptista CardiaMarana, Aparecido Nilceu [UNESP]2019-10-06T17:05:38Z2019-10-06T17:05:38Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject594-601http://dx.doi.org/10.1007/978-3-030-13469-3_69Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11401 LNCS, p. 594-601.1611-33490302-9743http://hdl.handle.net/11449/19020210.1007/978-3-030-13469-3_692-s2.0-85063066921Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/190202Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:52:04.632057Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
3D face recognition with reconstructed faces from a collection of 2D images |
title |
3D face recognition with reconstructed faces from a collection of 2D images |
spellingShingle |
3D face recognition with reconstructed faces from a collection of 2D images Neto, João Baptista Cardia 3D face recognition 3DLBP Biometrics Face reconstruction |
title_short |
3D face recognition with reconstructed faces from a collection of 2D images |
title_full |
3D face recognition with reconstructed faces from a collection of 2D images |
title_fullStr |
3D face recognition with reconstructed faces from a collection of 2D images |
title_full_unstemmed |
3D face recognition with reconstructed faces from a collection of 2D images |
title_sort |
3D face recognition with reconstructed faces from a collection of 2D images |
author |
Neto, João Baptista Cardia |
author_facet |
Neto, João Baptista Cardia Marana, Aparecido Nilceu [UNESP] |
author_role |
author |
author2 |
Marana, Aparecido Nilceu [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Neto, João Baptista Cardia Marana, Aparecido Nilceu [UNESP] |
dc.subject.por.fl_str_mv |
3D face recognition 3DLBP Biometrics Face reconstruction |
topic |
3D face recognition 3DLBP Biometrics Face reconstruction |
description |
Nowadays, there is an increasing need for systems that can accurately and quickly identify a person. Traditional identification methods utilize something a person knows or something a person has. This kind of methods has several drawbacks, being the main one the fact that it is impossible to detect an imposter who uses genuine credentials to pass as a genuine person. One way to solve these kinds of problems is to utilize biometric identification. The face is one of the biometric features that best suits the covert identification. However, in general, biometric systems based on 2D face recognition perform very poorly in unconstrained environments, common in covert identification scenarios, since the input images present variations in pose, illumination, and facial expressions. One way to mitigate this problem is to use 3D face data, but the current 3D scanners are expensive and require a lot of cooperation from people being identified. Therefore, in this work, we propose an approach based on local descriptors for 3D Face Recognition based on 3D face models reconstructed from collections of 2D images. Initial results show 95% in a subset of the LFW Face dataset. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T17:05:38Z 2019-10-06T17:05:38Z 2019-01-01 |
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/10.1007/978-3-030-13469-3_69 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11401 LNCS, p. 594-601. 1611-3349 0302-9743 http://hdl.handle.net/11449/190202 10.1007/978-3-030-13469-3_69 2-s2.0-85063066921 |
url |
http://dx.doi.org/10.1007/978-3-030-13469-3_69 http://hdl.handle.net/11449/190202 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11401 LNCS, p. 594-601. 1611-3349 0302-9743 10.1007/978-3-030-13469-3_69 2-s2.0-85063066921 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
594-601 |
dc.source.none.fl_str_mv |
Scopus 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 |
|
_version_ |
1808128713439576064 |