Self-Supervised Temperature Representation Learning for Fever Screening

Utilizing thermal infrared facial imaging for fever screening in public spaces has become a common strategy to curb the spread of influenza viruses. However, it is difficult to capture larger number of faces with fever labels, which makes learning facial temperature representation extremely difficult. To overcome this limitation, we propose a self-supervised fever screening framework (SelfFS) to learn temperature representation from infrared face images. Specifically, SelfFS employs rate reduction theory to guide the network to focus on temperature features by expanding the coding rate of faces with different temperatures and compressing the coding rate of faces with the same temperature but different appearances. Furthermore, we impose sparsity constraints on the network parameters, which facilitates the extraction of simple temperature features with a limited number of neurons while filtering complex appearance features. Experiments demonstrate that our SelfFS framework outperforms existing fever screening techniques and achieves the comparable results with the supervised methods.