Face Mask Detection Using Deep Learning

ABSTRACT

With the spread of coronavirus disease 2019 (COVID-19) pandemic throughout the world, social distancing and using a face mask have become crucial to prevent the spreading of this disease. Our goal is to develop a better way to detect face masks. In this paper, we propose a comparison between all available networks, which is an efficient one-stage face mask detector. The detection scheme follows preprocessing, feature extraction, and classification. The mask detector has been built using deep learning, specifically ResNetV2, as the base pre-trained model upon which we have our own CNN. We use OpenCV’s ImageNet to extract faces from video frames and our trained model to classify if the person is wearing a mask or not. We also propose an object removal algorithm to reject prediction below absolute confidence and accept only predictions above it. For the training purpose, we are using the face mask dataset, which consists of 680 images with mask and 686 images without mask. The results show mask detector has an accuracy of 99.9%. We have also used other pre-trained networks like MobileNetV2 as our base network and compared our results. ResNet50 gives us the state-of-the-art performance of face mask detection, which is higher than other face detectors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.