Emotional Valence Recognition, Analysis of Salience and Eye Movements

Abstract

This paper studies the performance of recorded eye movements and computational visual attention models (i.e. saliency models) in the recognition of emotional valence of an image. In the first part of this study, it employs eye movement data (fixation & saccade) to build image content descriptors and use them with support vector machines to classify the emotional valence. In the second part, it examines if the human saliency map can be substituted with the state-of-the-art computational visual attention models in the task of valence recognition. The results indicate that the eye movement based descriptors provide significantly better performance compared to the baselines, which apply low-level visual cues (e.g. color, texture and shape). Furthermore, it will be shown that the current computational models for visual attention are not able to capture the emotional information in similar extent as the real eye movements.