Robust realtime face recognition and tracking system

There s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when...

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Detalhes bibliográficos
Autores: Chen, Kai, Zhao, Le Jun
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2009
País:Argentina
Recursos:Universidad Nacional de La Plata
Repositorio:SEDICI (UNLP)
Idioma:inglés
OAI Identifier:oai:sedici.unlp.edu.ar:10915/9655
Acesso em linha:http://sedici.unlp.edu.ar/handle/10915/9655
Access Level:acceso abierto
Palavra-chave:Ciencias Informáticas
meanshift
svm
wavelet
realtime face detection
realtime face tracking
face recognition
Kalman filter
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spelling Robust realtime face recognition and tracking systemChen, KaiZhao, Le JunCiencias Informáticasmeanshiftsvmwaveletrealtime face detectionrealtime face trackingface recognitionKalman filterThere s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use Boosted Cascade combined with skin model for face detection and then in order to recognize the candidate faces, they will be analyzed by the hybrid Wavelet, PCA (principle component analysis) and SVM (support vector machine) method. After that, Meanshift and Kalman filter will be invoked to track the face. The experimental results show that the algorithm has quite good performance in terms of real-time and accuracy.Facultad de Informática2009-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf82-88http://sedici.unlp.edu.ar/handle/10915/9655enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct09-6.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2024-05-08T12:30:05Zoai:sedici.unlp.edu.ar:10915/9655Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292024-05-08 12:30:06.079SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Robust realtime face recognition and tracking system
title Robust realtime face recognition and tracking system
spellingShingle Robust realtime face recognition and tracking system
Chen, Kai
Ciencias Informáticas
meanshift
svm
wavelet
realtime face detection
realtime face tracking
face recognition
Kalman filter
title_short Robust realtime face recognition and tracking system
title_full Robust realtime face recognition and tracking system
title_fullStr Robust realtime face recognition and tracking system
title_full_unstemmed Robust realtime face recognition and tracking system
title_sort Robust realtime face recognition and tracking system
dc.creator.none.fl_str_mv Chen, Kai
Zhao, Le Jun
author Chen, Kai
author_facet Chen, Kai
Zhao, Le Jun
author_role author
author2 Zhao, Le Jun
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
meanshift
svm
wavelet
realtime face detection
realtime face tracking
face recognition
Kalman filter
topic Ciencias Informáticas
meanshift
svm
wavelet
realtime face detection
realtime face tracking
face recognition
Kalman filter
description There s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use Boosted Cascade combined with skin model for face detection and then in order to recognize the candidate faces, they will be analyzed by the hybrid Wavelet, PCA (principle component analysis) and SVM (support vector machine) method. After that, Meanshift and Kalman filter will be invoked to track the face. The experimental results show that the algorithm has quite good performance in terms of real-time and accuracy.
publishDate 2009
dc.date.none.fl_str_mv 2009-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9655
url http://sedici.unlp.edu.ar/handle/10915/9655
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct09-6.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
82-88
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 15,81155

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