用潜在空间对齐提升人脸识别中的公平性，减少数据偏见影响。
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment
- 通过潜在空间对齐技术，缓解面部表情识别中的数据偏见。
- 在多个公开数据集上验证，模型公平性显著提升，准确率也有所改善。
- 适合关注算法公平性与可解释性的研究人员和开发者。
基于计算机视觉的自动面部表情识别已成研究热点。作为监督学习任务，面部表情识别（FER）依赖大量包含不同社会文化背景的数据。过去十年提出的多个真实场景下的公开数据集，主要通过众包或网络爬取方式获取，且采用人工标注情感意图，固有地引入了个体偏见。此外，这些数据集在不同社会文化群体中代表性不足，导致类别不平衡问题。尽管偏差分析与缓解已在多个领域被研究，但在面部表情识别领域仍较少关注。本文利用基于潜在空间的表征学习方法，减轻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。
原文摘要 · Abstract (English)
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.
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