平衡天平：利用隐空间对齐增强面部表情识别的公平性
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment
AI总结：
针对真实FER数据集因人工标注和群体表征不均导致的人口统计偏见与类别不平衡问题，本文提出基于隐空间的表征学习方法，以提升面部表情识别模型的公平性和整体准确率。
摘要：
利用面部表情自动识别情绪意图一直是计算机视觉领域中一个被深入研究的课题。面部表情识别（Facial Expression Recognition，FER）作为一项监督学习任务，严重依赖于能够体现各种社会文化人口统计属性的大量数据。在过去十年中，已有若干通过众包或网络爬取收集的真实场景野外FER数据集被提出。然而，这些实际使用的数据集中，大多数采用人工标注方法来标记情绪意图，这本质上会传播个体人口统计偏见。此外，这些数据集也缺乏对各种社会文化人口统计群体的公平表征，从而导致类别不平衡。偏见分析及其缓解已在多个领域和问题设置中得到研究，但在FER领域，这仍是一个探索相对较少的方向。本工作利用基于隐空间的表征学习来缓解面部表情识别系统中的偏见，从而提升深度学习模型的公平性和整体准确率。
英文摘要：
Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-the-wild FER datasets that have been proposed were collected through crowd-sourcing or web-scraping. However, most of these practically used datasets employ a manual annotation methodology for labeling emotional intent, which inherently propagates individual demographic biases. Moreover, these datasets also lack an equitable representation of various socio-cultural demographic groups, thereby inducing a class imbalance. Bias analysis and its mitigation have been investigated across multiple domains and problem settings, however, in the FER domain, this is a relatively lesser explored area. This work leverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems, thereby enhancing a deep learning model's fairness and overall accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
