Citation

Asghar, Muhammad Zubair and Albogamy, Fahad R. and Al-Rakhami, Mabrook S. and Asghar, Junaid and Rahmat, Mohd Khairil and Alam, Muhammad Mansoor and Lajis, Adidah and Mohamad Nasir, Haidawati (2022) Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic. Frontiers in Public Health, 10. ISSN 2296-2565

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Abstract

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).

Item Type:	Article
Uncontrolled Keywords:	Neural Network, Convolutional Neural Network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	22 Jul 2022 01:22
Last Modified:	22 Jul 2022 01:22
URII:	http://shdl.mmu.edu.my/id/eprint/10152

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