Experiences accelerating features selection in Viola-Jones algorithm

Autores
Lescano, Germán Ezequiel; Santana Mansilla, Pablo; Costaguta, Rosanna
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Faces and facial expressions recognition is an interesting topic for researchers in machine vision. Viola-Jones algorithm is the most spread algorithm for this task. Building a classification model for face recognition can take many years if the implementation of its training phase is not appropriately optimized. In this study, several settings for implementing the training phase are analyzed. The aim was to share our experiences when we try to accelerate the training phase using one computer with a graphical processing unit (GPU). For each setting, the execution times were analyzed and compared with previous studies. Although we don't contribute to break new ground in topic or methodology, we decide to share our experience in order to show an antecedent working with a cheap GPU with the aim that this can be useful to another for to make comparisons.
XIV Workshop Computación Gráfica, Imágenes y Visualización (WCGIV).
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Adaboost
Viola-Jones algorithm
CUDA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/55806
Acceder
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network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling Experiences accelerating features selection in Viola-Jones algorithmLescano, Germán EzequielSantana Mansilla, PabloCostaguta, RosannaCiencias InformáticasAdaboostViola-Jones algorithmCUDAFaces and facial expressions recognition is an interesting topic for researchers in machine vision. Viola-Jones algorithm is the most spread algorithm for this task. Building a classification model for face recognition can take many years if the implementation of its training phase is not appropriately optimized. In this study, several settings for implementing the training phase are analyzed. The aim was to share our experiences when we try to accelerate the training phase using one computer with a graphical processing unit (GPU). For each setting, the execution times were analyzed and compared with previous studies. Although we don't contribute to break new ground in topic or methodology, we decide to share our experience in order to show an antecedent working with a cheap GPU with the aim that this can be useful to another for to make comparisons.XIV Workshop Computación Gráfica, Imágenes y Visualización (WCGIV).Red de Universidades con Carreras en Informática (RedUNCI)2016-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf281-290http://sedici.unlp.edu.ar/handle/10915/55806enginfo:eu-repo/semantics/reference/hdl/10915/55718info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:38:30Zoai:sedici.unlp.edu.ar:10915/55806Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:38:31.01SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Experiences accelerating features selection in Viola-Jones algorithm
title Experiences accelerating features selection in Viola-Jones algorithm
spellingShingle Experiences accelerating features selection in Viola-Jones algorithm
Lescano, Germán Ezequiel
Ciencias Informáticas
Adaboost
Viola-Jones algorithm
CUDA
title_short Experiences accelerating features selection in Viola-Jones algorithm
title_full Experiences accelerating features selection in Viola-Jones algorithm
title_fullStr Experiences accelerating features selection in Viola-Jones algorithm
title_full_unstemmed Experiences accelerating features selection in Viola-Jones algorithm
title_sort Experiences accelerating features selection in Viola-Jones algorithm
dc.creator.none.fl_str_mv Lescano, Germán Ezequiel
Santana Mansilla, Pablo
Costaguta, Rosanna
author Lescano, Germán Ezequiel
author_facet Lescano, Germán Ezequiel
Santana Mansilla, Pablo
Costaguta, Rosanna
author_role author
author2 Santana Mansilla, Pablo
Costaguta, Rosanna
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Adaboost
Viola-Jones algorithm
CUDA
topic Ciencias Informáticas
Adaboost
Viola-Jones algorithm
CUDA
dc.description.none.fl_txt_mv Faces and facial expressions recognition is an interesting topic for researchers in machine vision. Viola-Jones algorithm is the most spread algorithm for this task. Building a classification model for face recognition can take many years if the implementation of its training phase is not appropriately optimized. In this study, several settings for implementing the training phase are analyzed. The aim was to share our experiences when we try to accelerate the training phase using one computer with a graphical processing unit (GPU). For each setting, the execution times were analyzed and compared with previous studies. Although we don't contribute to break new ground in topic or methodology, we decide to share our experience in order to show an antecedent working with a cheap GPU with the aim that this can be useful to another for to make comparisons.
XIV Workshop Computación Gráfica, Imágenes y Visualización (WCGIV).
Red de Universidades con Carreras en Informática (RedUNCI)
description Faces and facial expressions recognition is an interesting topic for researchers in machine vision. Viola-Jones algorithm is the most spread algorithm for this task. Building a classification model for face recognition can take many years if the implementation of its training phase is not appropriately optimized. In this study, several settings for implementing the training phase are analyzed. The aim was to share our experiences when we try to accelerate the training phase using one computer with a graphical processing unit (GPU). For each setting, the execution times were analyzed and compared with previous studies. Although we don't contribute to break new ground in topic or methodology, we decide to share our experience in order to show an antecedent working with a cheap GPU with the aim that this can be useful to another for to make comparisons.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/55806
url http://sedici.unlp.edu.ar/handle/10915/55806
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/reference/hdl/10915/55718
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
281-290
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution 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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