Abstract
Emotion recognition is a core element in the development of emotional intelligence in machines. Early models are designed to recognise full-blown emotions which are easily identifiable. However, we rarely experience these types of emotion in our daily lives. Most of the time, we have difficulty in identifying our own and others’ emotions with confidence: this is emotional ambiguity. At the root of the development of emotion recognition systems, databases should allow the introduction of ambiguity in the emotional representation. This paper summarises the main emotional representations and proposes a state of the art of the most used multimodal databases in emotion recognition, with a study of their position on the problem. The paper further discusses the possibility of representing the emotion ambiguity from the selected databases.
Article
Hélène Tran, Lisa Brelet, Issam Falih, Xavier Goblet, & Engelbert Mephu Nguifo (2022). L’ambiguïté dans la représentation des émotions : état de l’art des bases de données multimodales. Revue des Nouvelles Technologies de l’Information, Extraction et Gestion des Connaissances, RNTI-E-38, 87-98. https://editions-rnti.fr/?inprocid=1002719
Useful Links
Github repository: databases for multimodal emotion recognition and emotional ambiguity