Graph-Tabular Latent Fusion for Non-Contact Body Temperature Prediction from Thermal Facial Landmarks.

Non-contact body-temperature prediction from facial thermography is affected by pose, occlusion, missing measurements, and inter-subject variation. This study proposes a graph-tabular latent-representation fusion framework for predicting body temperature from thermal facial landmark profiles. A Pearson correlation coefficient (PCC)-guided landmark graph models landmark-to-landmark thermal dependencies. At the same time, the same landmark-temperature signal is retained as a tabular representation to preserve global temperature-pattern interactions. The graph and tabular branches are encoded independently, fused at the latent level, and trained for target-landmark temperature regression with auxiliary reconstruction losses. Experiments were conducted on TFD68 under complete, missing completely at random (MCAR), and structured missing not at random (MNAR) conditions. The structured MNAR simulation combines 3D head-pose visibility modelling, accessory-driven occlusion, validation against real TFD68 occlusion annotations, and graph-construction sensitivity analyses. Results show that selected fused configurations improve over strong stand-alone graph and tabular baselines, particularly under MNAR-imputed evaluation, with the best selected configuration reducing prediction error by approximately 6%. Statistical testing further confirms significant improvements in most MNAR fused-baseline comparisons. Accuracy-efficiency analysis shows that fusion improves robustness at the cost of additional inference time, providing a flexible design space for thermal landmark-based body-temperature prediction.