Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection

Autores
Lescano, Germán Ezequiel; Santana Mansilla, Pablo; Costaguta, Rosanna
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
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 optimized. In this study, we analyze different implementations for the training phase. The aim was to reduce the time needed during training phase when using one computer with a cheap graphical processing unit (GPU). The execution times were analyzed and compared with previous studies. Results showed that combining C language, CUDA, etc., it is possible to reach acceptable times for training phase. Further research may involve the measurement of the performance of our approach computers with better GPU capacity and exploring a multi-GPU approach.
Facultad de Informática
Materia
Ciencias Informáticas
feature selection
Algoritmos
CUDA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/59990
Acceder
id SEDICI_82c2a85562d59b3f7affbde064e03941
oai_identifier_str oai:sedici.unlp.edu.ar:10915/59990
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features SelectionLescano, Germán EzequielSantana Mansilla, PabloCostaguta, RosannaCiencias Informáticasfeature selectionAlgoritmosCUDAFaces 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 optimized. In this study, we analyze different implementations for the training phase. The aim was to reduce the time needed during training phase when using one computer with a cheap graphical processing unit (GPU). The execution times were analyzed and compared with previous studies. Results showed that combining C language, CUDA, etc., it is possible to reach acceptable times for training phase. Further research may involve the measurement of the performance of our approach computers with better GPU capacity and exploring a multi-GPU approach.Facultad de Informática2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf68-73http://sedici.unlp.edu.ar/handle/10915/59990enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-8.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:39:47Zoai:sedici.unlp.edu.ar:10915/59990Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:39:47.861SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
title Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
spellingShingle Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
Lescano, Germán Ezequiel
Ciencias Informáticas
feature selection
Algoritmos
CUDA
title_short Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
title_full Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
title_fullStr Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
title_full_unstemmed Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
title_sort Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
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
feature selection
Algoritmos
CUDA
topic Ciencias Informáticas
feature selection
Algoritmos
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 optimized. In this study, we analyze different implementations for the training phase. The aim was to reduce the time needed during training phase when using one computer with a cheap graphical processing unit (GPU). The execution times were analyzed and compared with previous studies. Results showed that combining C language, CUDA, etc., it is possible to reach acceptable times for training phase. Further research may involve the measurement of the performance of our approach computers with better GPU capacity and exploring a multi-GPU approach.
Facultad de Informática
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 optimized. In this study, we analyze different implementations for the training phase. The aim was to reduce the time needed during training phase when using one computer with a cheap graphical processing unit (GPU). The execution times were analyzed and compared with previous studies. Results showed that combining C language, CUDA, etc., it is possible to reach acceptable times for training phase. Further research may involve the measurement of the performance of our approach computers with better GPU capacity and exploring a multi-GPU approach.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
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/10915/59990
url http://sedici.unlp.edu.ar/handle/10915/59990
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/2017/05/JCST-44-Paper-8.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/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
68-73
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
_version_ 1842260262094110720
score 13.13397

Publicaciones similares