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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Autor*in:	Germán Ezequiel Lescano [verfasserIn] Pablo Santana Mansilla [verfasserIn] Rosanna Costaguta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	feature selection cuda viola-jones algorithm adaboost

Übergeordnetes Werk:	In: Journal of Computer Science and Technology - Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata, 2007, 17(2017), 01, Seite 68-73
Übergeordnetes Werk:	volume:17 ; year:2017 ; number:01 ; pages:68-73

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ069123195

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spelling	(DE-627)DOAJ069123195 (DE-599)DOAJee5215770d684549abb52787ea6374e0 DE-627 ger DE-627 rakwb eng TK7885-7895 QA75.5-76.95 Germán Ezequiel Lescano verfasserin aut Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. feature selection cuda viola-jones algorithm adaboost Computer engineering. Computer hardware Electronic computers. Computer science Pablo Santana Mansilla verfasserin aut Rosanna Costaguta verfasserin aut In Journal of Computer Science and Technology Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata, 2007 17(2017), 01, Seite 68-73 (DE-627)500637636 (DE-600)2205254-9 16666038 nnns volume:17 year:2017 number:01 pages:68-73 https://doaj.org/article/ee5215770d684549abb52787ea6374e0 kostenfrei https://journal.info.unlp.edu.ar/JCST/article/view/449 kostenfrei https://doaj.org/toc/1666-6046 Journal toc kostenfrei https://doaj.org/toc/1666-6038 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 01 68-73
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callnumber-first	T - Technology
author	Germán Ezequiel Lescano
spellingShingle	Germán Ezequiel Lescano misc TK7885-7895 misc QA75.5-76.95 misc feature selection misc cuda misc viola-jones misc algorithm misc adaboost misc Computer engineering. Computer hardware misc Electronic computers. Computer science Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
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topic_title	TK7885-7895 QA75.5-76.95 Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection feature selection cuda viola-jones algorithm adaboost
topic	misc TK7885-7895 misc QA75.5-76.95 misc feature selection misc cuda misc viola-jones misc algorithm misc adaboost misc Computer engineering. Computer hardware misc Electronic computers. Computer science
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title_sort	analysis of a gpu implementation of viola-jones’ algorithm for features selection
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title_auth	Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection
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Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection

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