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 Uncertainty-aware AU detection framework that explicitly models uncertainty at both stages. At the representation stage, we introduce CV-AFE, a conditional VAE (CVAE)-based AU feature extraction module that learns probabilistic AU representations by jointly estimating feature means and variances across multiple spatio-temporal scales; conditioning on AU labels further enables CV-AFE to capture uncertainty associated with inter-AU dependencies. At the decision stage, we design AB-ENN, an Asymmetric Beta Evidential Neural Network for multi-label AU detection, which parameterizes predictive uncertainty with Beta distributions and mitigates overconfidence via an asymmetric loss tailored to highly imbalanced binary labels. Extensive experiments on BP4D and DISFA show that UAU-Net achieves strong AU detection performance, and further analyses indicate that modeling uncertainty in both representation learning and evidential prediction improves robustness and reliability.

翻译：面部动作单元检测仍具挑战性，因其涉及表示阶段和决策阶段中由异构、AU特定不确定性引发的问题。现有方法虽改进了判别性特征学习，但常将AU表示视为确定性，忽视了视觉噪声、个体依赖的外观变化及模糊的AU间关系所导致的不确定性，这些因素均会显著降低鲁棒性。同时，传统点估计分类器提供的置信度校准较差，易产生过度自信预测，尤其在AU数据集典型的严重标签不平衡情形下。我们提出UAU-Net——一种在表示与决策阶段显式建模不确定性的AU检测框架。在表示阶段，我们引入CV-AFE——一种基于条件变分自编码器的AU特征提取模块，通过联合估计多时空尺度上的特征均值与方差来学习概率化AU表示；通过条件化AU标签，CV-AFE可进一步捕捉与AU间依赖性相关的不确定性。在决策阶段，我们设计AB-ENN——一种用于多标签AU检测的非对称Beta证据神经网络，其通过Beta分布参数化预测不确定性，并利用专为高度不平衡二值标签设计的非对称损失缓解过度自信问题。在BP4D和DISFA上的大量实验表明，UAU-Net实现了优异的AU检测性能，进一步分析证实，在表示学习与证据预测中建模不确定性可提升鲁棒性与可靠性。