Feature Extraction for Health Condition Analysis from Facial Thermal Images Considering Spatial Autocorrelation

Objective and convenient monitoring of health status is increasingly important for maintaining and enhancing well-being. Recent studies have attempted to estimate physiological and psychological states using facial thermal images, focusing on blood flow controlled by the autonomic nervous system. However, these studies often overlook the mechanisms underlying skin temperature formation. Skin temperature distribution is expected to exhibit spatial autocorrelation due to heat transfer between adjacent regions via blood flow. The objective of this study is to extract health-related features from facial thermal images by considering spatial autocorrelation. Facial thermal images linked to health questionnaire responses were analyzed to identify regions where spatial autocorrelation varies with health status. The results showed that poorer health correlated with lower spatial autocorrelation values around the eyes and nose, and a significant reduction in overall spatial autocorrelation. These findings suggest that spatial autocorrelation in facial thermal images may serve as a useful indicator of health status.