  Emotional expressions are the behaviors that communicate our emotional state
or attitude to others. They are expressed through verbal and non-verbal
communication. Complex human behavior can be understood by studying physical
features from multiple modalities; mainly facial, vocal and physical gestures.
Recently, spontaneous multi-modal emotion recognition has been extensively
studied for human behavior analysis. In this paper, we propose a new deep
learning-based approach for audio-visual emotion recognition. Our approach
leverages recent advances in deep learning like knowledge distillation and
high-performing deep architectures. The deep feature representations of the
audio and visual modalities are fused based on a model-level fusion strategy. A
recurrent neural network is then used to capture the temporal dynamics. Our
proposed approach substantially outperforms state-of-the-art approaches in
predicting valence on the RECOLA dataset. Moreover, our proposed visual facial
expression feature extraction network outperforms state-of-the-art results on
the AffectNet and Google Facial Expression Comparison datasets.
