Comparative study of human age estimation based on hand-crafted and deep face features
In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social...
| Autor: | |
|---|---|
| Tipo de documento: | dissertação |
| Data de publicação: | 2016 |
| País: | España |
| Recursos: | Universidad del País Vasco |
| Repositório: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/19054 |
| Acesso em linha: | http://hdl.handle.net/10810/19054 |
| Access Level: | Acceso aberto |
| Palavra-chave: | computer vision pattern recognition face image neural networks deep learning |
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Comparative study of human age estimation based on hand-crafted and deep face featuresBelver Mielgo, Carloscomputer visionpattern recognitionface imageneural networksdeep learningIn the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.Dornaika, FadiArganda Carreras, Ignacio201620162016info:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10810/19054reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglés2016;5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationaloai:addi.ehu.eus:10810/190542026-06-18T09:23:17Z |
| dc.title.none.fl_str_mv |
Comparative study of human age estimation based on hand-crafted and deep face features |
| title |
Comparative study of human age estimation based on hand-crafted and deep face features |
| spellingShingle |
Comparative study of human age estimation based on hand-crafted and deep face features Belver Mielgo, Carlos computer vision pattern recognition face image neural networks deep learning |
| title_short |
Comparative study of human age estimation based on hand-crafted and deep face features |
| title_full |
Comparative study of human age estimation based on hand-crafted and deep face features |
| title_fullStr |
Comparative study of human age estimation based on hand-crafted and deep face features |
| title_full_unstemmed |
Comparative study of human age estimation based on hand-crafted and deep face features |
| title_sort |
Comparative study of human age estimation based on hand-crafted and deep face features |
| dc.creator.none.fl_str_mv |
Belver Mielgo, Carlos |
| author |
Belver Mielgo, Carlos |
| author_facet |
Belver Mielgo, Carlos |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Dornaika, Fadi Arganda Carreras, Ignacio |
| dc.subject.none.fl_str_mv |
computer vision pattern recognition face image neural networks deep learning |
| topic |
computer vision pattern recognition face image neural networks deep learning |
| description |
In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features. |
| publishDate |
2016 |
| dc.date.none.fl_str_mv |
2016 2016 2016 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10810/19054 |
| url |
http://hdl.handle.net/10810/19054 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
2016;5 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Attribution-NonCommercial-ShareAlike 4.0 International |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Attribution-NonCommercial-ShareAlike 4.0 International |
| dc.format.none.fl_str_mv |
application/pdf |
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reponame:Addi. Archivo Digital para la Docencia y la Investigación instname:Universidad del País Vasco |
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Universidad del País Vasco |
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Addi. Archivo Digital para la Docencia y la Investigación |
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Addi. Archivo Digital para la Docencia y la Investigación |
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1869420701848436736 |
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15,301603 |