Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning
Identifieur interne : 000797 ( Main/Exploration ); précédent : 000796; suivant : 000798Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning
Auteurs : Sulayman Ahmed [Tunisie] ; Mondher Frikha [Tunisie] ; Taha Darwassh Hanawy Hussein [Iraq] ; Javad Rahebi [Turquie]Source :
- BioMed Research International [ 2314-6133 ] ; 2021.
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Abstract
In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.
Url:
DOI: 10.1155/2021/6621540
PubMed: 33778071
PubMed Central: 7969091
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uniqKey="Hussein T" first="Taha Darwassh Hanawy" last="Hussein">Taha Darwassh Hanawy Hussein</name><affiliation wicri:level="1"><nlm:aff id="I2">Kirkuk University, Kirkuk, Iraq</nlm:aff><country xml:lang="fr">Iraq</country><wicri:regionArea>Kirkuk University, Kirkuk</wicri:regionArea></affiliation></author><author><name sortKey="Rahebi, Javad" sort="Rahebi, Javad" uniqKey="Rahebi J" first="Javad" last="Rahebi">Javad Rahebi</name><affiliation wicri:level="1"><nlm:aff id="I3">Department of Software Engineering, Istanbul Ayvansaray University, Istanbul, Turkey</nlm:aff><country xml:lang="fr">Turquie</country><wicri:regionArea>Department of Software Engineering, Istanbul Ayvansaray University, Istanbul</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">33778071</idno><idno type="pmc">7969091</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969091</idno><idno type="RBID">PMC:7969091</idno><idno 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Tunisia</nlm:aff><country xml:lang="fr">Tunisie</country><wicri:regionArea>ENETCOM, Universite de Sfax</wicri:regionArea></affiliation></author><author><name sortKey="Hussein, Taha Darwassh Hanawy" sort="Hussein, Taha Darwassh Hanawy" uniqKey="Hussein T" first="Taha Darwassh Hanawy" last="Hussein">Taha Darwassh Hanawy Hussein</name><affiliation wicri:level="1"><nlm:aff id="I2">Kirkuk University, Kirkuk, Iraq</nlm:aff><country xml:lang="fr">Iraq</country><wicri:regionArea>Kirkuk University, Kirkuk</wicri:regionArea></affiliation></author><author><name sortKey="Rahebi, Javad" sort="Rahebi, Javad" uniqKey="Rahebi J" first="Javad" last="Rahebi">Javad Rahebi</name><affiliation wicri:level="1"><nlm:aff id="I3">Department of Software Engineering, Istanbul Ayvansaray University, Istanbul, Turkey</nlm:aff><country xml:lang="fr">Turquie</country><wicri:regionArea>Department of Software Engineering, Istanbul Ayvansaray University, Istanbul</wicri:regionArea></affiliation></author></analytic><series><title level="j">BioMed Research International</title><idno type="ISSN">2314-6133</idno><idno type="eISSN">2314-6141</idno><imprint><date when="2021">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Databases, Factual (MeSH)</term><term>Deep Learning (MeSH)</term><term>Facial Recognition (MeSH)</term><term>Humans (MeSH)</term><term>Pattern Recognition, Automated (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage profond (MeSH)</term><term>Bases de données factuelles (MeSH)</term><term>Humains (MeSH)</term><term>Reconnaissance automatique des formes (MeSH)</term><term>Reconnaissance faciale (MeSH)</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Databases, Factual</term><term>Deep Learning</term><term>Facial Recognition</term><term>Humans</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage profond</term><term>Bases de données factuelles</term><term>Humains</term><term>Reconnaissance automatique des formes</term><term>Reconnaissance faciale</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. 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Feng</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Iraq</li><li>Tunisie</li><li>Turquie</li></country></list><tree><country name="Tunisie"><noRegion><name sortKey="Ahmed, Sulayman" sort="Ahmed, Sulayman" uniqKey="Ahmed S" first="Sulayman" last="Ahmed">Sulayman Ahmed</name></noRegion><name sortKey="Frikha, Mondher" sort="Frikha, Mondher" uniqKey="Frikha M" first="Mondher" last="Frikha">Mondher Frikha</name></country><country name="Iraq"><noRegion><name sortKey="Hussein, Taha Darwassh Hanawy" sort="Hussein, Taha Darwassh Hanawy" uniqKey="Hussein T" first="Taha Darwassh Hanawy" last="Hussein">Taha Darwassh Hanawy Hussein</name></noRegion></country><country name="Turquie"><noRegion><name sortKey="Rahebi, Javad" sort="Rahebi, Javad" uniqKey="Rahebi J" first="Javad" last="Rahebi">Javad Rahebi</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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