Personality-Aware Personalized Emotion Recognition from Physiological Signals.

Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors. The latent correlation among different subjects has also been rarely examined. We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we assign each of them with weights. The emotion relevance learned on the vertex-weighted multi-modal multi-task hypergraphs is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method.