Eyes Recognition for Inner Canthus Temperature Detection using YOLOv5 Algorithm

Viral infections severely attack the physically frail elderly population, resulting in fatal drawbacks. The fact of having massive elderly population growth in Europe gives high priority to the detection of physical frailty and infectious diseases. This paper presents a safe, accurate, fast temperature detection system that could be integrated into homes or assisted living residences. The presented work aims to detect one of the symptoms of contagious diseases: elevated body temperature. In order to do so, we worked on recognizing eyes in thermal face images followed by scanning the detected eyes region for inner canthus temperature. Eyes detection was done by training four different sizes of You Only Look Once 5
^th
version (YOLOv5) object detection algorithm: nano, small, medium and large. A total of 4,255 thermal images were implemented for the training process after merging two different datasets and applying data augmentation techniques. Results show a similar mAP score (99.5%) for the different trained models. The large YOLOv5 model was the fastest, working at 115 FPS.