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

• Published in: Energy and buildings Vol. 298; p. 113495
• Main Authors: Jeoung, Jaewon, Jung, Seunghoon, Hong, Taehoon, Lee, Minhyun, Koo, Choongwan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2023
• Subjects: Face landmark detection; Machine learning; Occupant-centric control; Thermal comfort prediction; Thermal image; Face landmark detection; Occupant-centric control; Thermal image; Machine learning; Thermal comfort prediction
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2023.113495

•Predict thermal comfort non-invasively from skin temperature features.•Identify ROIs of face component based on 68-point face landmarks in thermal images.•Achieves 90.26% accuracy and withstands diverse head pose conditions.•Enhance occupant-centric control and better thermal comfort prediction. 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.