Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition

Liam Schoneveld; Alice Othmani; Hazem Abdelkawy

doi:10.1016/j.patrec.2021.03.007

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arXiv · 2103.09154

Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition

Liam Schoneveld·Alice Othmani·Hazem Abdelkawy

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Abstract

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.

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BibTeX RIS

Liam Schoneveld, Alice Othmani, Hazem Abdelkawy. 2021-03-16. Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition. https://doi.org/10.1016/j.patrec.2021.03.007

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