Open Access

ARTICLE

Face Mask Recognition for Covid-19 Prevention

Trong Hieu Luu¹, Phan Nguyen Ky Phuc^2,*, Zhiqiu Yu³, Duy Dung Pham¹, Huu Trong Cao¹
1 College of Engineering, Can Tho University, Can Tho city, 910900, Viet Nam
2 International University-Vietnam National University, Vietnam National University, HoChiMinh City, 70000, Vietnam
3 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
* Corresponding Author: Phan Nguyen Ky Phuc. Email:

Computers, Materials & Continua 2022, 73(2), 3251-3262. https://doi.org/10.32604/cmc.2022.029663

Received 09 March 2022; Accepted 08 May 2022; Issue published 16 June 2022

Abstract

In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection. This simple method can reduce up to 95% of the spread of the particles. However, this solution depends heavily on social consciousness, which is sometimes unstable. In order to improve the effectiveness of wearing face masks in public spaces, this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask. The approach uses the deep learning technique that relies on GoogleNet, AlexNet, and VGG16 models. The results are synthesized by an ensemble method, i.e., the bagging technique. From the experimental results, the approach represents a more than 95% accuracy of face mask recognition.

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Keywords

Face mask detection; deep learning; AlexNet; GoogLeNet; VGG16; ensemble method

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