FeverMamba: Highlight Fever in Crowd via Mamba for Remote Fever Screening

Remote fever screening based on thermal infrared images can screen feverish faces in real time and play an important role in vital sign monitoring. Screening methods that rely on short-term crowd facial temperature differences can overcome environmental effects without the need for additional sensors, but effectively encoding these differences to highlight fever face remains a challenge. To this end, we develop a fever screening framework based on Mamba, which exploits the context capturing capability of Mamba to model the temperature differences of thermal infrared face images. Furthermore, considering that local contextual associations in the crowd will limit the construction of global differences, we propose a shuffle scanning method to break the local contextual associations and construct multiple shuffle scanning to achieve global difference representation. In addition, we design a temperature-aware self-supervised loss function to cope with the situation where data of fever faces is difficult to collect. Finally, we achieve state-of-the-art performance in both supervised and self-supervised cases on the thermal infrared face dataset.