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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

Arpit Jain1, Nageswara Rao Moparthi1, A. Swathi2, Yogesh Kumar Sharma1, Nitin Mittal3, Ahmed Alhussen4, Zamil S. Alzamil5,*, MohdAnul Haq5

1 Department of Computer Science and Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram,522302, India
2 Department of Computer Science and Engineering, Sreyas Institute of Engineering and Technology, Hyderabad,500068, India
3 University Centre for Research and Development, Chandigarh University, Mohali, 140413, India
4 Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
5 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia

* Corresponding Author: Zamil S. Alzamil. Email: email

(This article belongs to the Special Issue: Intelligent Devices and Computing Applications)

Computer Systems Science and Engineering 2024, 48(2), 341-362. https://doi.org/10.32604/csse.2023.036973

Abstract

Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks. To contribute to society, we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization. The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50 (ResNet-50) architecture and works on detection localization. The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks (AlexNet), Residual Neural Networks (ResNet), and Visual Geometry Group Networks (VGG-Net) advanced architecture. The proposed system generates an accuracy of 98.4% when modeled using Residual Neural Network 50 (ResNet-50). Also, the precision and recall values are proved as better when compared to the existing models. This outstanding work also can be used in video surveillance applications.

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APA Style
Jain, A., Moparthi, N.R., Swathi, A., Sharma, Y.K., Mittal, N. et al. (2024). Deep learning-based mask identification system using resnet transfer learning architecture. Computer Systems Science and Engineering, 48(2), 341-362. https://doi.org/10.32604/csse.2023.036973
Vancouver Style
Jain A, Moparthi NR, Swathi A, Sharma YK, Mittal N, Alhussen A, et al. Deep learning-based mask identification system using resnet transfer learning architecture. Comput Syst Sci Eng. 2024;48(2):341-362 https://doi.org/10.32604/csse.2023.036973
IEEE Style
A. Jain et al., "Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture," Comput. Syst. Sci. Eng., vol. 48, no. 2, pp. 341-362. 2024. https://doi.org/10.32604/csse.2023.036973



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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