Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

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....

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 27798 - 19
Main Authors: Gao, Guang, Chen, Chuangchuang, Xu, Kun, Liu, Kai, Mashhadi, Arsam
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.11.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/166/987
Accuracy
Algorithms
Bidirectional recurrent neural network
Databases, Factual
Deep Learning
Ebola virus
Ebolavirus
Face
Face detection
Hemorrhagic Fever, Ebola - diagnosis
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted - methods
Improved Ebola optimization search algorithm
Information processing
multidisciplinary
Multidisciplinary research
Neural networks
Neural Networks, Computer
Pattern formation
Pattern Recognition, Automated - methods
Science
Science (multidisciplinary)
Deep learning
Bidirectional recurrent neural network
Improved Ebola optimization search algorithm
Face detection
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-79067-x