Real-time Face Mask Detection Using Deep Learning on Embedded Systems

• Main Authors: Lopez, Vidal Wyatt M, Abu, Patricia Angela R, Estuar, Ma. Regina Justina E
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: COVID-19; deep learning; pandemics; facial recognition; neural networks; organizations; real-time systems; face mask detection; embedded systems; computer vision; convolutional neural network; Computer Sciences; Databases and Information Systems; Public Health
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/247; https://ieeexplore.ieee.org/document/9664684
• Institution: Ateneo De Manila University

Coronavirus disease (COVID-19) is an infectious disease; which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was identified in December 2019 in Wuhan; China [1]; [2]. It is a pandemic that causes respiratory disorder and is transmitted through sneezing droplets of infected individuals. These droplets can fall on the objects around the effected and enter a healthy individual through contact. Major symptoms of this disease include lethargy; dry cough; followed by fever [3]. The number of cases is surging dramatically; raping developed and undeveloped countries together [3]. According to the World Health Organization (WHO) COVID-19 weekly epidemiological Update for 29 th of December there are 79 million infected cases and 1.7 million deaths globally. This pandemic not only affects our health but also affects our livelihood. In the absence of specific treatment or a vaccine; non-pharmaceutical interventions (NPI) form the backbone of the response to the COVID-19 pandemic. These NPI includes physical distancing; regular hand washing; and wearing a face mask. This study aims to help with the monitoring of these NPIs specifically wearing face masks using deep learning. This study implements face mask detection and recognition system that automatically detects and recognizes if a person is wearing a Medically approved face mask; Non-Medically approved face mask; or not wearing a mask at all. This study has determined that MobileNetV1 model has shown the best performance regarding classification (79%) and processing speed up to 3.25 fps.