论文标题
调查面部表情识别中的偏见和公平性
Investigating Bias and Fairness in Facial Expression Recognition
论文作者
论文摘要
在情感计算和计算机视觉领域中，对情绪和情感的表达的认识是一个充分研究的研究问题，其中包含大量数据集，其中包含面部图像和相应的表达标签。但是，实际上，这些数据集几乎没有被考虑到人口中的公平分布。因此，在这项工作中，我们通过比较了两个众所周知的数据集（RAF-DB和Celeba），通过比较三种不同的方法，即基线，一种属性和分解方法来对面部表达识别的偏见和公平性进行系统的研究。我们的结果表明：（i）数据增强提高了基线模型的准确性，但仅此单单就无法减轻偏见效应； （ii）就准确性和公平而言，用数据增强的属性感知和通过数据增强的分解方法表现出色； （iii）解散方法是减轻人口偏见的最佳方法； （iv）偏置缓解策略更适合存在不均匀的属性分布或子组数据数量不平衡。
Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding expression labels. However, virtually none of these datasets have been acquired with consideration of fair distribution across the human population. Therefore, in this work, we undertake a systematic investigation of bias and fairness in facial expression recognition by comparing three different approaches, namely a baseline, an attribute-aware and a disentangled approach, on two well-known datasets, RAF-DB and CelebA. Our results indicate that: (i) data augmentation improves the accuracy of the baseline model, but this alone is unable to mitigate the bias effect; (ii) both the attribute-aware and the disentangled approaches fortified with data augmentation perform better than the baseline approach in terms of accuracy and fairness; (iii) the disentangled approach is the best for mitigating demographic bias; and (iv) the bias mitigation strategies are more suitable in the existence of uneven attribute distribution or imbalanced number of subgroup data.
