UAU-Net: Uncertainty-aware Representation Learning and Evidential Classification for Facial Action Unit Detection
Facial action unit (AU) detection remains challenging because it involves heterogeneous, AU-specific uncertainties arising at both the representation and decision stages. Recent methods have improved discriminative feature learning, but they often treat the AU representations as deterministic, overlooking uncertainty caused by visual noise, subject-dependent appearance variations, and ambiguous inter-AU relationships, all of which can substantially degrade robustness. Meanwhile, conventional point-estimation classifiers often provide poorly calibrated confidence, producing overconfident predictions, especially under the severe label imbalance typical of AU datasets. We propose UAU-Net, an...
| Authors | Yuze Li, Zhilei Liu |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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