A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL
. In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2021-10-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/277 |
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| _version_ | 1823858335872450560 |
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| author | LAWRENCE O. OMOTOSHO IBRAHIM K. OGUNDOYIN JOSHUA O. OYENIYI OLUWASHINA A. OYENIRAN |
| author_facet | LAWRENCE O. OMOTOSHO IBRAHIM K. OGUNDOYIN JOSHUA O. OYENIYI OLUWASHINA A. OYENIRAN |
| author_sort | LAWRENCE O. OMOTOSHO |
| collection | DOAJ |
| description |
. In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a method of using a neural network model that is first trained on a problem similar to the problem that is being solved. The most commonly used face recognition methods are mainly based on template matching, geometric features based, algebraic and deep learning method. The advantage of template matching is that it is easy to implement, and the disadvantage is that it is difficult to deal with the pose and scale changes effectively. The most important issue, regardless of the method used in the face recognition system, is dimensionality and computational complexity, especially when operating on large databases. In this paper, we applied a transfer learning model based on AlexNet Deep convolutional network to develop a real time face recognition system that has a good robustness to face pose and illumination, reduce dimensionality, complexity and improved recognition accuracy. The system has a recognition accuracy of 98.95 %.
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| format | Article |
| id | doaj-art-53d0f8e4df5349d6811f56b877af7caa |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-53d0f8e4df5349d6811f56b877af7caa2025-02-11T11:40:13ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322021-10-0127210.29081/jesr.v27i2.277A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODELLAWRENCE O. OMOTOSHOIBRAHIM K. OGUNDOYINJOSHUA O. OYENIYIOLUWASHINA A. OYENIRAN . In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a method of using a neural network model that is first trained on a problem similar to the problem that is being solved. The most commonly used face recognition methods are mainly based on template matching, geometric features based, algebraic and deep learning method. The advantage of template matching is that it is easy to implement, and the disadvantage is that it is difficult to deal with the pose and scale changes effectively. The most important issue, regardless of the method used in the face recognition system, is dimensionality and computational complexity, especially when operating on large databases. In this paper, we applied a transfer learning model based on AlexNet Deep convolutional network to develop a real time face recognition system that has a good robustness to face pose and illumination, reduce dimensionality, complexity and improved recognition accuracy. The system has a recognition accuracy of 98.95 %. https://jesr.ub.ro/index.php/1/article/view/277transfer learning, convolutional neural network, deep learning |
| spellingShingle | LAWRENCE O. OMOTOSHO IBRAHIM K. OGUNDOYIN JOSHUA O. OYENIYI OLUWASHINA A. OYENIRAN A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL Journal of Engineering Studies and Research transfer learning, convolutional neural network, deep learning |
| title | A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL |
| title_full | A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL |
| title_fullStr | A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL |
| title_full_unstemmed | A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL |
| title_short | A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL |
| title_sort | real time face recognition system using alexnet deep convolutional network transfer learning model |
| topic | transfer learning, convolutional neural network, deep learning |
| url | https://jesr.ub.ro/index.php/1/article/view/277 |
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