The performance of a computer vision model depends on the size and quality of
its training data. Recent studies have unveiled previously-unknown composition
biases in common image datasets which then lead to skewed model outputs, and
have proposed methods to mitigate these biases. However, most existing works
assume that human-generated annotations can be considered gold-standard and
unbiased. In this paper, we reveal that this assumption can be problematic, and
that special care should be taken to prevent models from learning such
annotation biases. We focus on facial expression recognition and compare the
label biases between lab-controlled and in-the-wild datasets. We demonstrate
that many expression datasets contain significant annotation biases between
genders, especially when it comes to the happy and angry expressions, and that
traditional methods cannot fully mitigate such biases in trained models. To
remove expression annotation bias, we propose an AU-Calibrated Facial
Expression Recognition (AUC-FER) framework that utilizes facial action units
(AUs) and incorporates the triplet loss into the objective function.
Experimental results suggest that the proposed method is more effective in
removing expression annotation bias than existing techniques.

本文针对面部表情识别领域的标签偏差问题，提出了一种利用面部动作单元进行带有三元损失的 AU 校准面部表情识别（AUC-FER）框架，实验证明该方法比现有技术更有效。