Automatic Recognition of Emotions and Membership in Group Videos

Wenxuan Mou, Hatice Gunes, Ioannis Patras; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 27-35

Automatic affect analysis and understanding has become a well established research area in the last two decades. However, little attention has been paid to the analysis of the affect expressed in group settings, either in the form of affect expressed by the whole group collectively or affect expressed by each individual member of the group. This paper presents a framework which, in group settings automatically classifies the affect expressed by each individual group member along both arousal and valence dimensions. We first introduce a novel vQLZM-FV descriptor to represent the facial behaviours of individuals in the spatio-temporal domain and then propose a method to recognize the group membership of each individual by using their face and body behavioural cues. The experiments show that the proposed vQLZM-FV outperforms the other feature representations in affect recognition, and group membership can be recognized using the non-verbal face and body features.