Real World Face Mask Detection using MobileNetV2 and Raspberry Pi

Abstract

On March 12, 2020, Corona Virus Disease 2019 (COVID 19) was declared a global pandemic. Because of its quick spread from one person to another, this disease was thought to be more hazardous. Face masks have proven to be a good and effective way to stop the spread of COVID 19. Detection of Face Mask is a challenging problem. This paper proposes the method to solve this challenge by using deep learning. This work uses Multi-Task Cascaded Convolutional Neural Network (MTCNN) for detection and identification of face. MobileNetV2 is used as an object detector for mask detection. A total of 3833 images from different data sources were chosen for this work. This is later implemented using Raspberry Pi and pi cam, this setup transmits live video data from a remote location and hence the prediction of wearing mask is accomplished. The amount of information lost in the process is decreased gradually at 20th epoch is 0.0199. The accuracy by which the mask/no mask detection is increased