Manuscript Number : CSEIT1217519

Face Mask Recognition Using MobileNetV2

Authors(2) :-Vatsal Patel, Dhruti Patel

The pandemic of Corona Virus Disease is generating a public health emergency. Wearing a mask is one of the most efficient ways to combat the infection. This paper presents the detection of face masks, through mitigating, evaluating, preventing, and preparing actions regarding COVID-19. In this work, face mask identification is achieved using Machine Learning technique and the Image Classification algorithms are MobileNetV2 with major changes which includes Label Binarizer, ImageNet, and Binary Cross-Entropy. The methods involved in building the model are collecting the data, pre-processing, image generation, model construction, compilation, and finally testing. The proposed method can recognize people with and without masks. The training accuracy of the proposed method is 98.5% and the testing accuracy is 99%. This model is implemented in an image or video stream to detect faces with mask.

Authors and Affiliations

Vatsal Patel
Devang Patel Institute of Advance Technology and Research, Charusat University, Gujarat, India
Dhruti Patel
Devang Patel Institute of Advance Technology and Research, Charusat University, Gujarat, India

Deep Learning, CNN, MobileNetV2, Face Mask, COVID-19

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Publication Details

Published in : Volume 7 | Issue 5 | September-October 2021
Date of Publication : 2021-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 35-42
Manuscript Number : CSEIT1217519
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Vatsal Patel, Dhruti Patel, "Face Mask Recognition Using MobileNetV2", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 5, pp.35-42, September-October-2021. Available at doi : https://doi.org/10.32628/CSEIT1217519
Journal URL : https://res.ijsrcseit.com/CSEIT1217519 Citation Detection and Elimination     |      |          | BibTeX | RIS | CSV

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