Face recognition using artificial neural networks in parallel architecture
Face detection and recognition is the main aspect for different important areas such as video surveillance, biometrics, interactive game applications, human computer interaction and access control systems. These systems require fast real time detection and recognition with high recognition rate. In...
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Asian Research Publishing Network
2023
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| _version_ | 1833349823868174336 |
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| author | Omarov B. Suliman A. Kushibar K. |
| author2 | 57202103462 |
| author_facet | 57202103462 Omarov B. Suliman A. Kushibar K. |
| author_sort | Omarov B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Face detection and recognition is the main aspect for different important areas such as video surveillance, biometrics, interactive game applications, human computer interaction and access control systems. These systems require fast real time detection and recognition with high recognition rate. In this paper we propose implementation of the Artificial Neural Network by using high performance computing architecture based on Graphics Processing Unit to get face recognition with high accuracy and more speedup. There, we consider a parallel training approach for backpropagation algorithm for face recognition. For the high performance of face recognition it was used Compute Unified Device Architecture (CUDA) on a GPU. The experimental results demonstrate a significant decrease on executing times and greater speedup than serial implementation. � 2005 - 2016 JATIT & LLS. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-22653 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Asian Research Publishing Network |
| record_format | dspace |
| spelling | my.uniten.dspace-226532023-05-29T14:11:30Z Face recognition using artificial neural networks in parallel architecture Omarov B. Suliman A. Kushibar K. 57202103462 25825739000 57191381849 Face detection and recognition is the main aspect for different important areas such as video surveillance, biometrics, interactive game applications, human computer interaction and access control systems. These systems require fast real time detection and recognition with high recognition rate. In this paper we propose implementation of the Artificial Neural Network by using high performance computing architecture based on Graphics Processing Unit to get face recognition with high accuracy and more speedup. There, we consider a parallel training approach for backpropagation algorithm for face recognition. For the high performance of face recognition it was used Compute Unified Device Architecture (CUDA) on a GPU. The experimental results demonstrate a significant decrease on executing times and greater speedup than serial implementation. � 2005 - 2016 JATIT & LLS. All rights reserved. Final 2023-05-29T06:11:30Z 2023-05-29T06:11:30Z 2016 Article 2-s2.0-84989339650 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989339650&partnerID=40&md5=5c103fbdf4526516c5a4d2906c7b9b9a https://irepository.uniten.edu.my/handle/123456789/22653 91 2 238 248 Asian Research Publishing Network Scopus |
| spellingShingle | Omarov B. Suliman A. Kushibar K. Face recognition using artificial neural networks in parallel architecture |
| title | Face recognition using artificial neural networks in parallel architecture |
| title_full | Face recognition using artificial neural networks in parallel architecture |
| title_fullStr | Face recognition using artificial neural networks in parallel architecture |
| title_full_unstemmed | Face recognition using artificial neural networks in parallel architecture |
| title_short | Face recognition using artificial neural networks in parallel architecture |
| title_sort | face recognition using artificial neural networks in parallel architecture |
| url_provider | http://dspace.uniten.edu.my/ |