Papers › Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit...
Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
Cheng Luo, Siyang Song, Weicheng Xie, Linlin Shen, Hatice Gunes
The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU's activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs' relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | Average AUC | 83.1 | #4 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | Average F1 | 65.5 | #4 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | Average AUC | 82.6 | #5 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | Average F1 | 64.7 | #5 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | BP4D | Swin-B | Average F1 | 62.6 | #7 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | BP4D | ResNet 50 | Average F1 | 59.1 | #9 of 10 | Archive leaderboard | report |
| Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | Average AUC | 92.9 | #5 of 8 | Archive leaderboard | report |
| Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | Average F1 | 63.1 | #5 of 8 | Archive leaderboard | report |
| Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | Average AUC | 92.1 | #6 of 8 | Archive leaderboard | report |
| Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | Average F1 | 62.4 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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