• Automatic face detection based...

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

Title:
Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.
Authors:
Gao G; School of Network Engineering, Zhoukou Normal University, ZhouKou, 466001, Henan, China., Chen C; School of Network Engineering, Zhoukou Normal University, ZhouKou, 466001, Henan, China., Xu K; School of Network Engineering, Zhoukou Normal University, ZhouKou, 466001, Henan, China. zknugx@163.com., Liu K; School of Software, Chongqing Finance and Economics College, Chongqing, 401320, China., Mashhadi A; University of Tehran, Tehran, Iran. arsammashhadi@gmail.com.; College of Technical Engineering, The Islamic University, Najaf, Iraq. arsammashhadi@gmail.com.
Source:
Scientific reports [Sci Rep] 2024 Nov 13; Vol. 14 (1), pp. 27798. Date of Electronic Publication: 2024 Nov 13.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
Image Processing, Computer-Assisted*/methods , Neural Networks, Computer* , Algorithms*, Pattern Recognition, Automated/methods ; Hemorrhagic Fever, Ebola/diagnosis ; Humans ; Face ; Deep Learning ; Databases, Factual
References:
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Comput Math Methods Med. 2021 Nov 8;2021:5595180. (PMID: 34790252)
IEEE Trans Image Process. 2010 Jun;19(6):1635-50. (PMID: 20172829)
IEEE Trans Pattern Anal Mach Intell. 2021 Nov;43(11):4008-4020. (PMID: 32750774)
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Open Med (Wars). 2020 Sep 08;15(1):860-871. (PMID: 33336044)
Contributed Indexing:
Keywords: Bidirectional recurrent neural network; Deep learning; Face detection; Improved Ebola optimization search algorithm
Entry Date(s):
Date Created: 20241113 Date Completed: 20241113 Latest Revision: 20241116
Update Code:
20260130
PubMed Central ID:
PMC11561261
DOI:
10.1038/s41598-024-79067-x
PMID:
39537839
Database:
MEDLINE

*Further Information*

*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.
(© 2024. The Author(s).)*

*Declarations Competing interests The authors declare no competing interests.*