Thermal image modification with facial arterial structure for physiological state assessment

Conventional region-of-interest (ROI) methods for analysing facial thermography to assess physiological states often struggle with inter-individual variability. This paper proposes a novel method that mitigates this issue by modifying thermal images based on a standardised facial arterial structure. The method’s effectiveness was validated using a long-term dataset of thermal images labelled with self-reported health conditions. Results showed that the proposed method, combined with Gaussian smoothing, extracts features that are highly generalisable across participants. Notably, higher-order statistics of the thermal distribution along the vasculature – specifically skewness and kurtosis – were identified as robust indicators of health state, outperforming conventional approaches. This anatomically informed approach provides a powerful and interpretable alternative to traditional ROI and Black-box deep learning methods for human health monitoring.