Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers

The problem of machine emotional intelligence is very broad and multifaceted; one of its challenges being the very fact that is hard to define it in an unambiguous way. There is no unique definition of emotion, and there is neither a specific method nor a particular required dataset that is guaranteed to capture it. One of the most popular emotion definitions is the one of the six basic emotions by Paul Ekman [1]. The original six emotions he proposed are: anger, disgust, fear, happiness, sadness and surprise. Another very popular approach is the 2-dimensional emotion map, where each emotional state is projected on the orthogonal axes of valence and arousal [2]. A third dimension can be added to this space with the axis of dominance, see [3] and its related references.