Facial action unit (AU) detection is a fundamental block for objective facial
expression analysis. Supervised learning approaches require a large amount of
manual labeling which is costly. The limited labeled data are also not diverse
in terms of gender which can affect model fairness. In this paper, we propose
to use synthetically generated data and multi-source domain adaptation (MSDA)
to address the problems of the scarcity of labeled data and the diversity of
subjects. Specifically, we propose to generate a diverse dataset through
synthetic facial expression re-targeting by transferring the expressions from
real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection
knowledge from a real dataset and the synthetic dataset to a target dataset.
Instead of aligning the overall distributions of different domains, we propose
Paired Moment Matching (PM2) to align the features of the paired real and
synthetic data with the same facial expression. To further improve gender
fairness, PM2 matches the features of the real data with a female and a male
synthetic image. Our results indicate that synthetic data and the proposed
model improve both AU detection performance and fairness across genders,
demonstrating its potential to solve AU detection in-the-wild.

通过使用合成数据和多源领域自适应方法解决已标记数据稀缺和性别多样性问题，本文提出了一种基于面部表情再目标化方法的面部动作单元检测模型，并采用配对矩匹配方法来提高性别平等性，实现实现在真实环境中的面部动作单元检测。

利用合成数据进行通用且公平的面部行为单元检测

Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit  Detection

This study investigates gender fairness in personalized pain care
recommendations using machine learning algorithms. Leveraging a contextual
bandits framework, personalized recommendations are formulated and evaluated
using LinUCB algorithm on a dataset comprising interactions with $164$ patients
across $10$ sessions each. Results indicate that while adjustments to algorithm
parameters influence the quality of pain care recommendations, this impact
remains consistent across genders. However, when certain patient information,
such as self-reported pain measurements, is absent, the quality of pain care
recommendations for women is notably inferior to that for men.

研究通过机器学习算法调查个性化疼痛护理建议中的性别公平性。利用上下文强化学习框架，使用 LinUCB 算法对包括 164 个患者在每个 10 个会话中的交互的数据集进行个性化的建议制定和评估。结果表明，虽然调整算法参数会影响疼痛护理建议的质量，但这种影响在性别之间保持一致。然而，当某些患者信息（例如自报疼痛指标）缺失时，女性的疼痛护理建议质量明显低于男性。