Utilizing deep learning and 3DLBP for 3D Face recognition
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-75193-1_17 http://hdl.handle.net/11449/179599 |
Resumo: | Methods based on biometrics can help prevent frauds and do personal identification in day-to-day activities. Automated Face Recognition is one of the most popular research subjects since it has several important properties, such as universality, acceptability, low costs, and covert identification. In constrained environments methods based on 2D features can outperform the human capacity for face recognition but, once occlusion and other types of challenges are presented, the aforementioned methods do not perform so well. To deal with such problems 3D data and deep learning based methods can be a solution. In this paper we propose the utilization of Convolutional Neural Networks (CNN) with low-level 3D local features (3DLBP) for face recognition. The 3D local features are extracted from depth maps captured by a Kinect sensor. Experimental results on Eurecom database show that this proposal is promising, since, in average, almost 90% of the faces were correctly recognized. |
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Repositório Institucional da UNESP |
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Utilizing deep learning and 3DLBP for 3D Face recognition3D face recognition3D local featuresBiometricsConvolutional neural networksDeep learningDepth mapsKinectMethods based on biometrics can help prevent frauds and do personal identification in day-to-day activities. Automated Face Recognition is one of the most popular research subjects since it has several important properties, such as universality, acceptability, low costs, and covert identification. In constrained environments methods based on 2D features can outperform the human capacity for face recognition but, once occlusion and other types of challenges are presented, the aforementioned methods do not perform so well. To deal with such problems 3D data and deep learning based methods can be a solution. In this paper we propose the utilization of Convolutional Neural Networks (CNN) with low-level 3D local features (3DLBP) for face recognition. The 3D local features are extracted from depth maps captured by a Kinect sensor. Experimental results on Eurecom database show that this proposal is promising, since, in average, almost 90% of the faces were correctly recognized.São Carlos Federal University - UFSCARUNESP - São Paulo State UniversityUNESP - São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Cardia Neto, João BaptistaMarana, Aparecido Nilceu [UNESP]2018-12-11T17:35:59Z2018-12-11T17:35:59Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject135-142http://dx.doi.org/10.1007/978-3-319-75193-1_17Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 135-142.1611-33490302-9743http://hdl.handle.net/11449/17959910.1007/978-3-319-75193-1_172-s2.0-85042219158Scopusreponame: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)0,295info:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/179599Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:20:12.468242Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Utilizing deep learning and 3DLBP for 3D Face recognition |
title |
Utilizing deep learning and 3DLBP for 3D Face recognition |
spellingShingle |
Utilizing deep learning and 3DLBP for 3D Face recognition Cardia Neto, João Baptista 3D face recognition 3D local features Biometrics Convolutional neural networks Deep learning Depth maps Kinect |
title_short |
Utilizing deep learning and 3DLBP for 3D Face recognition |
title_full |
Utilizing deep learning and 3DLBP for 3D Face recognition |
title_fullStr |
Utilizing deep learning and 3DLBP for 3D Face recognition |
title_full_unstemmed |
Utilizing deep learning and 3DLBP for 3D Face recognition |
title_sort |
Utilizing deep learning and 3DLBP for 3D Face recognition |
author |
Cardia Neto, João Baptista |
author_facet |
Cardia Neto, João Baptista 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 |
Cardia Neto, João Baptista Marana, Aparecido Nilceu [UNESP] |
dc.subject.por.fl_str_mv |
3D face recognition 3D local features Biometrics Convolutional neural networks Deep learning Depth maps Kinect |
topic |
3D face recognition 3D local features Biometrics Convolutional neural networks Deep learning Depth maps Kinect |
description |
Methods based on biometrics can help prevent frauds and do personal identification in day-to-day activities. Automated Face Recognition is one of the most popular research subjects since it has several important properties, such as universality, acceptability, low costs, and covert identification. In constrained environments methods based on 2D features can outperform the human capacity for face recognition but, once occlusion and other types of challenges are presented, the aforementioned methods do not perform so well. To deal with such problems 3D data and deep learning based methods can be a solution. In this paper we propose the utilization of Convolutional Neural Networks (CNN) with low-level 3D local features (3DLBP) for face recognition. The 3D local features are extracted from depth maps captured by a Kinect sensor. Experimental results on Eurecom database show that this proposal is promising, since, in average, almost 90% of the faces were correctly recognized. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:35:59Z 2018-12-11T17:35:59Z 2018-01-01 |
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/10.1007/978-3-319-75193-1_17 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 135-142. 1611-3349 0302-9743 http://hdl.handle.net/11449/179599 10.1007/978-3-319-75193-1_17 2-s2.0-85042219158 |
url |
http://dx.doi.org/10.1007/978-3-319-75193-1_17 http://hdl.handle.net/11449/179599 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 135-142. 1611-3349 0302-9743 10.1007/978-3-319-75193-1_17 2-s2.0-85042219158 |
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) 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
135-142 |
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 |
|
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1808128792680464384 |