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

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...

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Autores principales: Lescano, Germán Ezequiel, Santana Mansilla, Pablo, Costaguta, Rosanna
Formato: Articulo
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Informáticas
feature selection
Algoritmos
CUDA
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/59990
http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-8.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-59990
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
feature selection
Algoritmos
CUDA
spellingShingle Ciencias Informáticas
feature selection
Algoritmos
CUDA
Lescano, Germán Ezequiel
Santana Mansilla, Pablo
Costaguta, Rosanna
Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
topic_facet Ciencias Informáticas
feature selection
Algoritmos
CUDA
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.
format Articulo
Articulo
author Lescano, Germán Ezequiel
Santana Mansilla, Pablo
Costaguta, Rosanna
author_facet Lescano, Germán Ezequiel
Santana Mansilla, Pablo
Costaguta, Rosanna
author_sort Lescano, Germán Ezequiel
title Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
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
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/59990
http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-8.pdf
work_keys_str_mv AT lescanogermanezequiel analysisofagpuimplementationofviolajonesalgorithmforfeaturesselection
AT santanamansillapablo analysisofagpuimplementationofviolajonesalgorithmforfeaturesselection
AT costagutarosanna analysisofagpuimplementationofviolajonesalgorithmforfeaturesselection
bdutipo_str Repositorios
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