Action Unit (AU) detection aims at automatically caracterizing facial expressions with the muscular activations they involve. Its main interest is to provide a low-level face representation that can be used to assist higher level affective computing tasks learning. Yet, it is a challenging task. Indeed, the available databases display limited face variability and are imbalanced toward neutral expressions. Furthermore, as AU involve subtle face movements they are difficult to annotate so that some of the few provided datapoints may be mislabeled. In this work, we aim at exploiting label smoothing ability to mitigate noisy examples impact by reducing confidence [1]. However, applying label smoothing as it is may aggravate imbalance-based pre-existing under-confidence issue and degrade performance. To circumvent this issue, we propose Robin Hood Label Smoothing (RHLS). RHLS principle is to restrain label smoothing confidence reduction to the majority class. In that extent, it alleviates both the imbalance-based over-confidence issue and the negative impact of noisy majority class examples. From an experimental standpoint, we show that RHLS provides a free performance improvement in AU detection. In particular, by applying it on top of a modern multi-task baseline we get promising results on BP4D and outperform state-of-the-art methods on DISFA.

翻译：动作单元（AU）检测旨在通过肌肉激活模式自动表征面部表情。其主要价值在于提供一种低层次的面部表征，辅助更高层次的情感计算任务学习。然而，这是一项极具挑战性的任务：现有数据库的面部多样性有限，且存在向中性表情倾斜的不平衡问题。此外，由于动作单元涉及细微的面部运动难以标注，少量已标注数据点可能存在错误标记。本研究旨在利用标签平滑技术降低置信度[1]，以减轻噪声样本的影响。但直接应用标签平滑可能加剧基于不平衡的预存置信度不足问题，导致性能下降。为解决此问题，我们提出罗宾汉标签平滑（RHLS）方法。RHLS的核心思想是将标签平滑的置信度降低仅限于多数类，从而同时缓解基于不平衡的过度自信问题以及多数类噪声样本的负面影响。实验表明，RHLS能无代价提升动作单元检测性能。特别是在现代多任务基线模型上应用该方法后，我们在BP4D数据集上取得了显著成果，并在DISFA数据集上超越了当前最优方法。