Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

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.

Erişime Açık
Görüntülenme
494
17.03.2022 tarihinden bu yana
İndirme
2
17.03.2022 tarihinden bu yana
Son Erişim Tarihi
03 Haziran 2024 20:19
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning
Yazar
(dc.contributor.author)
Cevat Rahebi
Yayın Yılı
(dc.date.issued)
2021
Tür
(dc.type)
Makale
Özet
(dc.description.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.
Açık Erişim Tarihi
(dc.date.available)
2024-03-21
Yayıncı
(dc.publisher)
Hındawı Ltd
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
Optimum Feature Selection
Konu Başlıkları
(dc.subject)
Particle Swarm Optimization
Konu Başlıkları
(dc.subject)
Deep Learning
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1378
ISSN
(dc.identifier.issn)
2314-6133
Dergi
(dc.relation.journal)
Bıomed Research Internatıonal
Esere Katkı Sağlayan
(dc.contributor.other)
Rahebi, J
Esere Katkı Sağlayan
(dc.contributor.other)
Frikha, M
Esere Katkı Sağlayan
(dc.contributor.other)
Hussein, TDH
Esere Katkı Sağlayan
(dc.contributor.other)
Ahmed, S
DOI
(dc.identifier.doi)
10.1155/2021/6621540
Orcid
(dc.identifier.orcid)
0000-0001-9875-4860
Dergi Cilt
(dc.identifier.volume)
2021
wosquality
(dc.identifier.wosquality)
Q3
wosauthorid
(dc.contributor.wosauthorid)
DNF-7937-2022
Department
(dc.contributor.department)
Yazılım Mühendisliği
Wos No
(dc.identifier.wos)
WOS:000631885300005
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
Veritabanları
(dc.source.platform)
PubMed
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.
Tamam

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms