Context-Aware Affective Graph Reasoning for Emotion Recognition

Affective computing has attracted researchers' attention in recent years. Emotion recognition is part of affective computing, which aims to recognize how the person feels, such as happy, sad, anger, disgust, fear, surprise. Traditional works about emotion recognition mainly focus on the characteristic of the person itself, such as audio, text, facial expression, body posture. However, the feelings of people can easily be affected by the context information. In this paper, we utilize the context to construct an affective graph to reason the emotional states. In detail, we detect the context using Region Proposal Network (RPN) to extract nodes as the input of the Graph Convolution Network (GCN), which transfers the convolution operation from Euclidean data structure to nonEuclidean data structure. The GCN learns the affective relationship during the back-propagation process. Moreover, the body feature is extracted by Convolution Neural Network (CNN). The output of GCN and CNN are combined finally to infer the discrete emotion categories and the continuous dimensions for VAD (Valence, Arouse, Dominance) measurement. Our method achieves higher performance than the baseline based on the EXOTIC dataset.