ABSTRACT
With the increase in the number of Covid-19 cases throughout the globe wearing face masks has proved to be effective in the prevention of the virus. In this work, we have originated a method that can detect if people are violating the rule of wearing a mask outdoors using a two-stage deep learning system. The first stage of the system detects different faces present in the input image using YOLO (You Only Look Once) model trained for the face detection and returns face ROIs. In the second stage extracted face ROI is passed through face mask detector model trained using MobileNetV2 which in turn classifies it as Mask or No mask. The dataset used for training the mask detector model is Real-World Masked Face Dataset (RMFD) and for Face Detection model is the WIDER dataset. The proposed method gives 98% accuracy for mask detection. The promising results derived from the proposed model demonstrate that the deployment of the model can be done in real-time systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.