TY - GEN
T1 - Face Mask Wearing Detection
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
AU - Ong, Jia You
AU - Ming Lim, Kian
AU - Lee, Chin Poo
AU - Chean Lee, Tze
AU - Tan, Shao Xian
AU - Yang Chia, Zi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Convolutional neural networks
KW - COVID-19
KW - Face mask wearing detection
UR - http://www.scopus.com/inward/record.url?scp=85174417906&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262558
DO - 10.1109/ICoICT58202.2023.10262558
M3 - Conference contribution
AN - SCOPUS:85174417906
T3 - International Conference on ICT Convergence
SP - 436
EP - 441
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
PB - IEEE Computer Society
Y2 - 23 August 2023 through 24 August 2023
ER -