An Empirical Study of Emotion Recognition from Thermal Video Based on Deep Neural Networks

Emotion recognition is a crucial problem in affective computing. Most of previous works utilized facial expression from visible spectrum data to solve emotion recognition task. Thermal videos provide temperature measurement of human body over time, which can be used to recognize affective states by learning its temporal pattern. In this paper, we conduct comparative experiments to study the effectiveness of the existing deep neural networks when applied to emotion recognition task from thermal video. We analyze the effect of various approaches for frame sampling in video, temporal aggregation between frames, and different convolutional neural network architectures. To the best of our knowledge, we are the first work to conduct study on emotion recognition from thermal video based on deep neural networks. Our work can provide preliminary study to design new methods for emotion recognition in thermal domain.