Thermal comfort prediction based on automated extraction of skin temperature of face component on thermal image

This paper proposes a framework for predicting thermal comfort based on the automated extraction of skin temperature features from thermal images. This aims to non-invasively collect thermal comfort information for occupant-centric control (OCC), significantly impacting energy consumption, health, and overall well-being. The proposed framework adopts 68-point face landmarks to identify regions of interest (ROIs) of face components in thermal images, and subsequently extracts skin temperature features from those ROIs to predict thermal comfort. To assess its performance, the face landmark detection performance was evaluated using various colormaps on thermal images. Furthermore, the validity of the proposed skin temperature features extraction from ROIs of face components was evaluated, determining which skin temperature features are effectively entered into machine learning models. Additionally, the reliability of the framework for predicting thermal comfort under different head pose conditions was evaluated to ensure its validity. The results revealed that the proposed framework achieved an accuracy rate of 90.26% and showed robustness even in the extreme head pose. The study's findings suggest that the proposed framework can make OCC more effective based on more accurate thermal comfort prediction using a single thermal camera device.