Implementing the Affective Mechanism for Group Emotion Recognition With a New Graph Convolutional Network Architecture

Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a powerful force shapes the group members' emotions. Current group emotion recognition methods focus on how the emotions of the group members form the group-level emotion but rarely take into account how the group emotion feeds back to the group members instantaneously. This paper proposes a new graph convolutional network architecture to characterize this unique affective mechanism for group emotion recognition. We regard the group members as the nodes of the graph and introduce a pseudo node into the graph to represent the role of the group. This paper uses graph convolutional networks to model the emotional interactions within the group from a static image and constructs an effective emotional representation at the group level for recognition. Experiment results on three widely used datasets for group emotion recognition show that our proposed method achieved superior performance in terms of recognition accuracy compared to the state-of-the-art methods.