In this paper, Histograms of Oriented Gradients dependent on the strong point of convolutional neural organization which is new methodology for evenness face data set, is introduced. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized Gabor wavelet change for include extraction of evenness face preparing information and afterward we utilized profound learning technique for acknowledgment. We executed and assessed the proposed strategy on ORL and YALE data sets with Matlab 2020b. Besides, similar trials were directed applying Particle Swarm Optimization (PSO) for include determination approach. The execution of Gabor wavelet include extraction with a high number of preparing picture tests has end up being more viable than different strategies in our examination. The acknowledgment rate while carrying out the PSO strategies on ORL data set is 86.62% while it is 92.6% with the three techniques on YALE data set. In any case, the utilization of PSO calculation has expanded the exactness rate to 95.88% for ORL information base and 95.23% on YALE data set.
Eser Adı (dc.title) | Face Recognition System using Histograms of Oriented Gradients and Convolutional Neural Network based on with Particle Swarm Optimization |
Yazar (dc.contributor.author) | Cevat Rahebi |
Yayın Yılı (dc.date.issued) | 2021 |
Yayıncı (dc.publisher) | Institute of Electrical and Electronics Engineers Inc. |
Tür (dc.type) | Bildiri |
Açık Erişim Tarihi (dc.date.available) | 2021-06-07 |
Özet (dc.description.abstract) | In this paper, Histograms of Oriented Gradients dependent on the strong point of convolutional neural organization which is new methodology for evenness face data set, is introduced. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized Gabor wavelet change for include extraction of evenness face preparing information and afterward we utilized profound learning technique for acknowledgment. We executed and assessed the proposed strategy on ORL and YALE data sets with Matlab 2020b. Besides, similar trials were directed applying Particle Swarm Optimization (PSO) for include determination approach. The execution of Gabor wavelet include extraction with a high number of preparing picture tests has end up being more viable than different strategies in our examination. The acknowledgment rate while carrying out the PSO strategies on ORL data set is 86.62% while it is 92.6% with the three techniques on YALE data set. In any case, the utilization of PSO calculation has expanded the exactness rate to 95.88% for ORL information base and 95.23% on YALE data set. |
DOI (dc.identifier.doi) | 10.1109/ICECCE52056.2021.9514139 |
ISBN (dc.identifier.isbn) | 9781665438971 |
Orcid (dc.identifier.orcid) | 0000-0001-9875-4860 |
Dil (dc.language.iso) | En |
Konu Başlıkları (dc.subject) | Convolutional neural network; Face recognition; Histograms of Oriented Gradients |
Konu Başlıkları (dc.subject) | Face recognition |
Konu Başlıkları (dc.subject) | Histograms of Oriented Gradients |
Esere Katkı Sağlayan (dc.contributor.other) | Rahebi, Javad |
Esere Katkı Sağlayan (dc.contributor.other) | Hussein, Taha Darwassh Hanawy |
Esere Katkı Sağlayan (dc.contributor.other) | Frikha, Mondher |
Esere Katkı Sağlayan (dc.contributor.other) | Ahmed, Sulayman |
Kitap Adı (dc.identifier.kitap) | 3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021 |
Department (dc.contributor.department) | Yazılım Mühendisliği |
Veritabanları (dc.source.platform) | Scopus |