用注意力机制与KL散度损失降低人脸识别偏见，提升公平性与准确率。
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention
- 结合双注意力机制与KL散度正则化，优化模型对不同群体的识别能力。
- 在多个数据集上实现性别与种族分类准确率显著提升，偏见明显降低。
- 适合关注AI公平性、人脸识别优化的研究者与工程师参考。
确保基于AI的人脸识别系统在所有人口群体中做出公正预测并表现一致至关重要。早期系统常表现出显著的性别与种族偏见，女性及深肤色个体的识别准确率较低。为解决此问题并促进人脸识别公平性，研究者提出了多种性别分类的偏见缓解技术。然而，仍面临数据多样性不足、公平性与准确率平衡困难、偏差测量不一致等挑战。本文提出一种基于预训练Inception-ResNet V1模型的方法，结合双注意力机制与KL散度正则化及交叉熵损失函数，在迁移学习框架下有效降低偏见，同时提升分类准确率与计算效率。实验结果表明，该方法在多个数据集上均显著改善了公平性与准确率，为缓解人脸识别偏见提供了有前景的解决方案。
原文摘要 · Abstract (English)
Ensuring that AI-based facial recognition systems produce fair predictions and work equally well across all demographic groups is crucial. Earlier systems often exhibited demographic bias, particularly in gender and racial classification, with lower accuracy for women and individuals with darker skin tones. To tackle this issue and promote fairness in facial recognition, researchers have introduced several bias-mitigation techniques for gender classification and related algorithms. However, many challenges remain, such as data diversity, balancing fairness with accuracy, disparity, and bias measurement. This paper presents a method using a dual attention mechanism with a pre-trained Inception-ResNet V1 model, enhanced by KL-divergence regularization and a cross-entropy loss function. This approach reduces bias while improving accuracy and computational efficiency through transfer learning. The experimental results show significant improvements in both fairness and classification accuracy, providing promising advances in addressing bias and enhancing the reliability of facial recognition systems.
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