A face mask detection system: An approach to fight with COVID‐19 scenario. (1st November 2022)

• Record Type: Journal Article
• Authors: Jayaswal, Ruchi; Dixit, Manish
• Is Part Of: Concurrency and computation. Volume 34:Number 28(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-01
• Subjects: 3D‐face masks -- CL‐SSDXcept -- COVID‐19 -- DNN models -- face mask detection -- hyperparameters -- optimizers; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7394
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: A new coronavirus has caused a pandemic crisis around the globe. According to the WHO, this is an infectious illness that spreads from person to person. Therefore, the only way to avoid this infection is to take precautions. Wearing a mask is the most critical COVID‐19 protection method because it prevents the virus from spreading from an infected person to a healthy one. This study reflects a deep learning method to create a system for detecting Face Masks. The paper proposes a unique FMDRT (Face Mask Dataset in Real‐Time) dataset to determine whether a person is wearing a mask or not. The RFMD and Face Mask datasets are also taken from the internet to evaluate the performance of the proposed method. The CLAHE preprocessing method is employed to enhance the image quality, then resizing and Image augmentation techniques are used to convert it into a standard format and increase the size of the dataset, respectively. The pretrained Caffe face detector model is used to detect the faces, and then the lightweight transfer learning‐based Xception model is applied for the feature extraction process. This paper recommended a novel model that is, CL‐SSDXcept to distinguish the Face Mask or no mask images. However, accession with the MobileNetV2, VGG16, VGG19, and InceptionV3 models with different hyperparameter settings has been tested on the FMDRT dataset. We have also compared the results of the synthesized dataset FMDRT to the existing Face Mask datasets. The experimental results attained 98% test accuracy on the suggested dataset 'FMDRT' using the CL‐SSDXcept method. The empirical findings have been reported at 50 iterations with tuned hyperparameter values with an average accuracy 98% and a loss of 0.05.