Real-Time Detection of Face Mask Using Convolutional Neural Network

Abstract

Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time.

Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application

“…The field of deep learning has undergone significant progressions in recent years, fundamentally altering our comprehension and approach to resolving a diverse range of intricate issues. These advancements have paved the way for breakthroughs in the domains of facial recognition [6], object detection [7], mask detection [8], machine translation [9], speech recognition [10], COVID-19 detection [11], and numerous others.…”