Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm
Abstract Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in...
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| Format: | Article |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79067-x |
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| author | Guang Gao Chuangchuang Chen Kun Xu Kai Liu Arsam Mashhadi |
| author_facet | Guang Gao Chuangchuang Chen Kun Xu Kai Liu Arsam Mashhadi |
| author_sort | Guang Gao |
| collection | DOAJ |
| description | Abstract Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches. |
| format | Article |
| id | doaj-art-8c3836464ba846fabfd664a31334635f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8c3836464ba846fabfd664a31334635f2024-11-17T12:18:52ZengNature PortfolioScientific Reports2045-23222024-11-0114111910.1038/s41598-024-79067-xAutomatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithmGuang Gao0Chuangchuang Chen1Kun Xu2Kai Liu3Arsam Mashhadi4School of Network Engineering, Zhoukou Normal UniversitySchool of Network Engineering, Zhoukou Normal UniversitySchool of Network Engineering, Zhoukou Normal UniversitySchool of Software, Chongqing Finance and Economics CollegeUniversity of TehranAbstract Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.https://doi.org/10.1038/s41598-024-79067-xFace detectionDeep learningBidirectional recurrent neural networkImproved Ebola optimization search algorithm |
| spellingShingle | Guang Gao Chuangchuang Chen Kun Xu Kai Liu Arsam Mashhadi Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm Scientific Reports Face detection Deep learning Bidirectional recurrent neural network Improved Ebola optimization search algorithm |
| title | Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm |
| title_full | Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm |
| title_fullStr | Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm |
| title_full_unstemmed | Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm |
| title_short | Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm |
| title_sort | automatic face detection based on bidirectional recurrent neural network optimized by improved ebola optimization search algorithm |
| topic | Face detection Deep learning Bidirectional recurrent neural network Improved Ebola optimization search algorithm |
| url | https://doi.org/10.1038/s41598-024-79067-x |
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