Face mask classification using convolutional neural networks with facial image regions and super resolution

Abstract

Face mask classification is relevant to public health and safety, so an approach for face mask classification using multi-task cascaded convolutional networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, blind super-resolution generative adversarial networks (BSRGAN), for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.