Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine

Face recognition is one of the most promising research area in the last decades. The SVM approach is one of the famous approaches in machine learning fields because it can determine the global optimum solutions with lesser number of training samples especially, complex non-linear challenges such as...

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Main Authors:	Saad, Wasan Kadhim, Jabbar, Waheb A., Abbas, Ahmed Abdul Rudah
Format:	Article
Language:	English
Published:	Medwell Journals 2019
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/1/Face%20Recognition%20Approach%20using%20an%20Enhanced%20Particle.pdf

id	ump-25612
recordtype	eprints
spelling	ump-256122019-08-07T03:33:52Z http://umpir.ump.edu.my/id/eprint/25612/ Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine Saad, Wasan Kadhim Jabbar, Waheb A. Abbas, Ahmed Abdul Rudah T Technology (General) Face recognition is one of the most promising research area in the last decades. The SVM approach is one of the famous approaches in machine learning fields because it can determine the global optimum solutions with lesser number of training samples especially, complex non-linear challenges such as in face recognition applications. Though, there is an important issue that can affects the whole classification process which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to discover the optimal parameters of SVM and many versions of PSO are used for this purpose, like: PSO-SVM technique, opposition PSO and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which represents adaptive acceleration PSO and SVM. In this study, a new hybrid technique based on the combination of "Accelerated PSO" and "OPSO-SVM" is introduced for face recognition applications. The hybridization can improve the convergence speed in PSO in order to find the optimal parameters of SVM. In the feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered to the proposed technique in order to classify the face images. Two human face datasets are used in the experimentation stage such as, SCface dataset and CASIA face dataset in order to validate the performance of the proposed technique. The comparison process for proposed technique with the other recent technique, like: PSO-SVM, OPSO-SVM and AAPSO-SVM is done as an assessment process. The proposed technique provided high accuracy for recognition when we compared it with the other techniques and it was robust in finding the optimal parameters of SVM. Medwell Journals 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25612/1/Face%20Recognition%20Approach%20using%20an%20Enhanced%20Particle.pdf Saad, Wasan Kadhim and Jabbar, Waheb A. and Abbas, Ahmed Abdul Rudah (2019) Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine. Journal of Engineering and Applied Sciences, 14 (9). pp. 2982-2987. ISSN 1816-949x (Print); 1818-7803 (Online) http://medwelljournals.com/abstract/?doi=jeasci.2019.2982.2987 http://dx.doi.org/10.3923/jeasci.2019.2982.2987
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Saad, Wasan Kadhim Jabbar, Waheb A. Abbas, Ahmed Abdul Rudah Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
description	Face recognition is one of the most promising research area in the last decades. The SVM approach is one of the famous approaches in machine learning fields because it can determine the global optimum solutions with lesser number of training samples especially, complex non-linear challenges such as in face recognition applications. Though, there is an important issue that can affects the whole classification process which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to discover the optimal parameters of SVM and many versions of PSO are used for this purpose, like: PSO-SVM technique, opposition PSO and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which represents adaptive acceleration PSO and SVM. In this study, a new hybrid technique based on the combination of "Accelerated PSO" and "OPSO-SVM" is introduced for face recognition applications. The hybridization can improve the convergence speed in PSO in order to find the optimal parameters of SVM. In the feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered to the proposed technique in order to classify the face images. Two human face datasets are used in the experimentation stage such as, SCface dataset and CASIA face dataset in order to validate the performance of the proposed technique. The comparison process for proposed technique with the other recent technique, like: PSO-SVM, OPSO-SVM and AAPSO-SVM is done as an assessment process. The proposed technique provided high accuracy for recognition when we compared it with the other techniques and it was robust in finding the optimal parameters of SVM.
format	Article
author	Saad, Wasan Kadhim Jabbar, Waheb A. Abbas, Ahmed Abdul Rudah
author_facet	Saad, Wasan Kadhim Jabbar, Waheb A. Abbas, Ahmed Abdul Rudah
author_sort	Saad, Wasan Kadhim
title	Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
title_short	Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
title_full	Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
title_fullStr	Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
title_full_unstemmed	Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
title_sort	face recognition approach using an enhanced particle swarm optimization and support vector machine
publisher	Medwell Journals
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/ http://umpir.ump.edu.my/id/eprint/25612/1/Face%20Recognition%20Approach%20using%20an%20Enhanced%20Particle.pdf
first_indexed	2023-09-18T22:39:25Z
last_indexed	2023-09-18T22:39:25Z
_version_	1777416814888747008

Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine

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