Face Detection in Thermal Images with Improved Spatial Precision and Temporal Stability

Thermal video can be used as a privacy-preserving and non-contact sensor for long-term health monitoring including respiratory activity. Face detection from thermal images is required to define the region of interest for automated respiration monitoring. In this study, we focus on thermal face detection using deep learning-based methods and transfer learning. First, YOLOv7, YOLOv7-tiny, and Detector Transformer (DeTr) object detection models were trained on an open thermal image dataset of faces. The weights from the pretrained models were transferred to a new model that was trained on our own target dataset. Results showed that transfer learning resulted in improved intersection-over-union (IoU) face detection performance. Moving beyond face detection in a single frame, we evaluated the stability of the trained face detection model with regard to the time-consistency of the detected bounding boxes in thermal videos. The DeTr model showed higher performance with 0.812 IoU and more stable predicted bounding boxes compared to YOLOv7 and YOLOv7-tiny. The proposed methods were also evaluated with regard to model size, as it pertains to viable deployment using edge computing, as part of a complete respiration rate estimation pipeline.