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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/25612/1/Face%20Recognition%20Approach%20using%20an%20Enhanced%20Particle.pdf
http://umpir.ump.edu.my/id/eprint/25612/
http://medwelljournals.com/abstract/?doi=jeasci.2019.2982.2987
http://dx.doi.org/10.3923/jeasci.2019.2982.2987
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/25612/1/Face%20Recognition%20Approach%20using%20an%20Enhanced%20Particle.pdf
http://umpir.ump.edu.my/id/eprint/25612/
http://medwelljournals.com/abstract/?doi=jeasci.2019.2982.2987
http://dx.doi.org/10.3923/jeasci.2019.2982.2987
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score 13.211869

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