Neural Networks for Emotion Recognition Based on Eye Tracking Data

Abstract

We present an approach for emotion recognition using information of the pupil. In last years, the pupil variables have been used as an assessment of emotional arousal. In this article, we generate signals of pupil size and gaze position monitored during image viewing. The emotions are provoked by visual stimuli of colored images. Those images were taken from the International Affective Picture System which has been the reference for objective emotional assessment based on visual stimuli. For recognising the emotions we use the evolution of the eye tracking data during a window of time. The learning dataset is composed by the evolution of the pupil size and the gaze position, and labels associated to the emotional states. We study two kinds of learning tools based on Neural Networks. We obtain promising empirical results that show the potential of using temporal learning tools for emotion recognition.