Emotion Recognition - A Tool to Improve Meeting Experience for Visually Impaired

Abstract

Facial expressions play an important role in human communication since they enrich spoken information and help convey additional sentiments e.g. mood. Among others, they non-verbally express a partner’s agreement or disagreement to spoken information. Further, together with the audio signal, humans can even detect nuances of changes in a person’s mood. However, facial expressions remain inaccessible to the blind and visually impaired, and also the voice signal alone might not carry enough mood information. Emotion recognition research mainly focused on detecting one of seven emotion classes. Such emotions are too detailed, and having an overall impression of primary emotional states such as positive, negative, or neutral is more beneficial for the visually impaired person in a lively discussion within a team. Thus, this paper introduces an emotion recognition system that allows a real-time detection of the emotions “agree”, “neutral”, and “disagree”, which are seen as the most important ones during a lively discussion. The proposed system relies on a combination of neural networks that allow extracting emotional states while leveraging the temporal information from videos.