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Multimodal emotion recognition has attracted great interest recently and numerous methodologies have been successfully investigated. However, the task requires the effective fusion multimodal representations in audio and video domains, and existing approaches still perform poorly on such a challenging task. This paper proposes a novel framework for recognizing emotion from multiple sources including facial expression, pose, body movements, and voice. In this framework, we first introduce new deep spatio-temporal features by cascading 3-dimensional convolution neural networks (C3Ds) and deep belief networks (DBNs) to effectively model spatial and temporal information presented in video and audio for emotion recognition. We subsequently propose a new feature-level fusion approach based on a bilinear pooling theory to combine the visual and audio feature vectors. The proposed fusion strategy …