Face Mask Detection: A Real-Time Android Application Based on Deep Learning Modeling

The accelerated spread of the COVID-19 (coronavirus) disease has put stress on healthcare systems. Some safety measures are provided, such as keeping social distance and wearing a mask, which can help curb transmission and save lives. This paper aims to detect whether a person is wearing a mask or not with video surveillance to enforce health and safety regulations in real-time. We propose a solution for face mask detection using two deep learning models, the MobileNetV2 and the Modified Convolutional Neural Network (MCNN). The trained models are converted to TensorFlow Lite to deploy an Android Application. Our models can achieve up to 99% accuracy. In this paper, an analysis of the number of individuals not wearing masks is provided by capturing the face and storing it on a mobile-backend-as-a-service. Our application can be adopted to increase health measures in real-time and control the spread of COVID-19.