FeverNet: Enabling accurate and robust remote fever screening

Abstract

Remote human fever screening via thermal infrared imaging helps reduce the risk of respiratory disease transmission and plays an important role in public health monitoring. However, the accuracy of such systems often falls prey to variations in measurement distance and environment temperature. Most previous methods tend to employ sensors to overcome these variations, which are expensive schemes and have limited performance improvement. To address above problems, this paper presents a novel and robust remote fever screening framework named FeverNet. Specifically, FeverNet introduces depth estimation network and temperature distribution constraints across time periods to reduce the influence of distance variations and environment temperature changes. The fever attention module is thus proposed to enhance feature representation and expand the difference between fever faces and normal ones. In addition, we provide the Extended Thermal Infrared Face dataset (ETIF), which further gives visible images (paired with thermal infrared images) for depth estimation and improve the fever face generated method based on the maximum temperature of the face. Extensive experiments on ETIF demonstrate the advantages of our FeverNet over the state-of-the-art methods.