A novel approach for bias mitigation of gender classification algorithms using consistency regularization
A novel approach for bias mitigation of gender classification algorithms using consistency regularization
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DOI：
10.1016/j.imavis.2023.104793
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Anoop Krishnan;A. Rattani
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文献类型：
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作者：
Anoop Krishnan;A. Rattani
已发表的研究证实，基于面部的自动性别分类算法在性别种族群体中存在偏见。具体来说，在基于面部的自动性别分类算法中，女性和深色皮肤的人的准确率不相等。为了减轻性别分类和其他基于面部分析的算法的偏见，视觉界提出了几种技术。然而，大多数现有的偏见缓解技术都缺乏通用性，需要一个人口统计学注释的训练集，是特定于应用程序的，并且经常在公平性和分类准确性之间进行权衡。这意味着公平性通常是以降低表现最好的人口统计子组的分类准确性为代价的。在本文中，我们提出了一种新的偏见缓解技术，该技术在自一致性设置中利用图像和特征级别的语义保留增强功能，用于下游性别分类任务。在性别标注的面部图像数据集上进行了彻底的实验验证，证实了我们的偏见缓解技术在提高总体性别分类准确性以及减少所有性别种族群体的偏见方面的有效性。具体来说，我们提出的技术比现有的偏见缓解技术平均减少了30%的偏差，并且比基线性别分类器的总体分类精度提高了约5%。因此，在数据集内部和跨数据集评估中产生最先进的泛化性能。此外，与大多数现有的偏见缓解技术相比，我们提出的技术在没有人口统计标签的情况下运行，并且与应用无关。
Published research has confirmed the bias of automated face-based gender classification algorithms across gender-racial groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people for face-based automated gender classification algorithms. To mitigate the bias of gender classification and other facial-analysis-based algorithms in general, the vision community has proposed several techniques. However, most of the existing bias mitigation techniques suffer from a lack of generalizability, need a demographically-annotated training set, are application-specific, and often offer a trade-off between fairness and classification accuracy. This means that fairness is often obtained at the cost of a reduction in the classification accuracy of the best-performing demographic sub-group. In this paper, we propose a novel bias mitigation technique that leverages the power of semantic preserving augmentations at the image-and feature-level in a self-consistency setting for the downstream gender classification task. Thorough experimental validation on gender-annotated facial image datasets confirms the efficacy of our bias mitigation technique in improving overall gender classification accuracy as well as reducing bias across all gender-racial groups over state-of-the-art bias mitigation techniques. Specifically, our proposed technique obtained a reduction in the bias by an average of 30% over existing bias mitigation techniques as well as an improvement in the overall classification accuracy of about 5% over the baseline gender classifier. Therefore, resulting in state-of-the-art generalization performance in the intra-and cross-dataset evaluations. Additionally, our proposed technique operates in the absence of demographic labels and is application agnostic, compared to most of the existing bias mitigation techniques.