Affect representation and recognition in 3D continuous valence–arousal–dominance space

Currently, the focus of research on human affect recognition has shifted from six basic emotions to complex affect recognition in continuous two or three dimensional space due to the following challenges: (i) the difficulty in representing and analyzing large number of emotions in one framework, (ii) the problem of representing complex emotions in the framework, and (iii) the lack of validation of the framework through measured signals, and (iv) the lack of applicability of the selected framework to other aspects of affective computing. This paper presents a Valence – Arousal – Dominance framework to represent emotions. This framework is capable of representing complex emotions on continuous 3D space. To validate the model, an affect recognition technique has been proposed that analyses spontaneous physiological (EEG) and visual cues. The DEAP dataset is a multimodal emotion dataset which contains video and physiological signals as well as Valence, Arousal and Dominance values. This dataset has been used for multimodal analysis and recognition of human emotions. The results prove the correctness and sufficiency of the proposed framework. The model has also been compared with other two dimensional models and the capacity of the model to represent many more complex emotions has been discussed.