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- Title
Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture.
- Authors
Jain, Arpit; Moparthi, Nageswara Rao; Swathi, A.; Sharma, Yogesh Kumar; Mittal, Nitin; Alhussen, Ahmed; Alzamil, Zamil S.; Haq, MohdAnul
- 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 amask 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 masksmandatory 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 fromResidualNeuralNetwork 50 (ResNet-50) architecture andworks on detection localization. The experiment is tested with other popular pre-trained models such asDeep Convolutional Neural Networks (AlexNet), Residual Neural Networks (ResNet), and Visual Geometry Group Networks (VGGNet) 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.
- Subjects
DEEP learning; WORLD Health Organization; CORONAVIRUS diseases; VIDEO surveillance; CONVOLUTIONAL neural networks
- Publication
Computer Systems Science & Engineering, 2024, Vol 48, Issue 2, p341
- ISSN
0267-6192
- Publication type
Article
- DOI
10.32604/csse.2023.036973