Face Mask Wearing Detection: A Comparative Analysis

Jia You Ong*, Kian Ming Lim, Chin Poo Lee, Tze Chean Lee, Shao Xian Tan, Zi Yang Chia

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

The COVID-19 pandemic has had a tremendous influence around the globe, impacting nearly every element of daily life. It has resulted in widespread illness and death, economic disruption, and changes in societal norms. Governments and organizations have applied various measures to slow the spread of the virus and mitigate its impacts. Among the most important mechanisms is the use of face masks to prevent the transmission and infection of COVID-19. This paper investigates and analyzes different machine learning (ML) methods to execute the classification task of categorizing faces into three classes: wearing masks, not wearing masks, or wearing masks improperly. The preprocessed and augmented dataset used in the study contains 4801 images with the dimension (50, 50, 3) and there are approximately 1500 faces for each class. According to the experimental results, convolutional neural networks (CNNs) can achieve 87% accuracy in classifying faces. These results indicate that CNNs outperform other ML methods, such as random forest, Naïve Bayes, and support vector machine.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages436-441
Number of pages6
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • Convolutional neural networks
  • COVID-19
  • Face mask wearing detection

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