面向公平的面部表情识别：改进的分布对齐方法
Toward Fair Facial Expression Recognition with Improved Distribution Alignment
AI总结：
针对面部表情识别模型存在的属性偏差问题，提出结合核均值收缩估计、MMD距离与对抗损失的分布对齐方法，并首次分析吸引力敏感属性，在两个数据集上实现准确率与公平性的提升。
摘要：
我们提出了一种缓解面部表情识别（FER）模型偏差的新方法。该方法旨在减少FER模型生成的嵌入向量中包含的性别、年龄、种族等敏感属性信息。我们采用核均值收缩估计器，在希尔伯特空间中估计与不同敏感属性组（如年轻组和老年组）相关的嵌入向量分布的核均值。基于这一估计，我们计算分布间的最大均值差异（MMD）距离，并将其与对抗损失一同纳入分类器损失，在学习过程中最小化该损失以优化分布对齐效果。该方法降低了模型对敏感属性的识别能力，进而提升公平性。此外，我们首次将吸引力概念作为FER模型中的重要敏感属性展开分析，证明FER模型确实会对更具吸引力的面孔表现出偏差。为验证模型在降低不同敏感属性（包括新提出的吸引力属性）相关偏差方面的有效性，我们在CelebA和RAF-DB这两个广泛使用的数据集上开展了多项实验。结果显示，该方法在准确率和公平性指标方面大多优于现有最优方法，证明了所提方法的有效性。
英文摘要：
We present a novel approach to mitigate bias in facial expression recognition (FER) models. Our method aims to reduce sensitive attribute information such as gender, age, or race, in the embeddings produced by FER models. We employ a kernel mean shrinkage estimator to estimate the kernel mean of the distributions of the embeddings associated with different sensitive attribute groups, such as young and old, in the Hilbert space. Using this estimation, we calculate the maximum mean discrepancy (MMD) distance between the distributions and incorporate it in the classifier loss along with an adversarial loss, which is then minimized through the learning process to improve the distribution alignment. Our method makes sensitive attributes less recognizable for the model, which in turn promotes fairness. Additionally, for the first time, we analyze the notion of attractiveness as an important sensitive attribute in FER models and demonstrate that FER models can indeed exhibit biases towards more attractive faces. To prove the efficacy of our model in reducing bias regarding different sensitive attributes (including the newly proposed attractiveness attribute), we perform several experiments on two widely used datasets, CelebA and RAF-DB. The results in terms of both accuracy and fairness measures outperform the state-of-the-art in most cases, demonstrating the effectiveness of the proposed method.
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