HEAT-MAP BASED EMOTION AND FACE RECOGNITION FROM THERMAL IMAGES

Nowadays, emotion recognition has become a feasible problem with implementation of Convolutional Neural Networks in Computer Vision domain. However, credibility of emotion recognition from daily images or videos is not enough. As people can easily mimic emotions one after another and fooling the trained models, a different approach should be taken into consideration. Thermal cameras would be a suitable way to develop more credible emotion recognition models. Heat-map of faces proved hinting emotions before, and it is not easy to fool the models trained from thermal heat-maps as it visualizes state of the body’s heat. In this research a method is adapted for training a model for recognizing emotions from thermal heat-mapped cameras with a fast detection algorithm -YOLOv3-. With this method the main aim is to detecting emotions from a given picture which taken from thermal cameras.