Papers › Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition
Liam Schoneveld, Alice Othmani, Hazem Abdelkawy
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | AffectNet | Distilled student | Accuracy (7 emotion) | 65.4 | #18 of 50 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | AffectNet | Distilled student | Accuracy (8 emotion) | 61.60 | #18 of 50 | Archive leaderboard | report |
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