A Scalable Analytics Pipeline for COVID-19 Face Mask Surveillance

Abstract

The COVID-19 coronavirus pandemic still causes a global health crisis. An effective protection method is using a face mask in public areas, according to the World Health Organization (WHO). Computer vision systems can be allies in monitoring public areas where the face mask is mandatory. However, face mask detection is challenging due to many factors, including diversity of people, facial features, head accessories, mask design, image position, and lighting changes. To tackle these issues, we present the following contributions: a new balanced face mask dataset named UTFPR-FMD1, consisting of 61,430 images splitted into “face” and “mask” classes; a transfer learning classification model for computer vision tasks, trained with our dataset; a new processing pipeline that allows face mask detection in video streams. Unlike available public datasets with imbalanced class distributions, the UTFPR-FMD1 contains images from different people, gender, and ages to minimize the training difficulty of deep learning models. We introduced a new measure to select valid images to perform inferences. Experimental results show the effectiveness of our model, outperforming the state-of-art methods for face mask detection tasks. Additionally, and different from other authors, we also present qualitative results. The system can detect heads with up to 60 degrees of rotation and process up to 10 FPS. In future work, we will deploy the current framework into production, perform tests in a near real-time environment, and extend it to process multiple video streams.