Most previous methods for emotion recognition focus on facial emotion and ignore the rich context information that implies important emotion states. To make full use of the contextual information to make up for the facial information, we propose the Context-Dependent Net (CD-Net) for robust context-aware human emotion recognition. Inspired by the long-range dependency of the transformer, we introduce the tubal transformer which forms the shared feature representation space to facilitate the interactions among the face, body, and context features. Besides, we introduce the hierarchical feature fusion to recombine the enhanced multi-scale face, body, and context features for emotion classification. Experimentally, we verify the effectiveness of the proposed CD-Net on the two large emotion datasets, CAER-S and EMOTIC. On the one hand, the quantitative evaluation results demonstrate the superiority of the proposed CD-Net over other state-of-the-art methods. On the other hand, the visualization results show CD-Net can capture the dependencies among the face, body, and context components and focus on the important features related to the emotion.
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