提出自适应校准方法，让人脸识别更准更公平。
Adaptive Calibration for Fair and Performant Facial Recognition
- 根据局部嵌入特征动态调整相似度概率，避免统一校准偏差。
- 在多个模型和数据集上同时提升准确率与公平性指标。
- 无需性别、种族等标注信息，适合真实场景部署。
我们提出自适应校准（Adaptive Calibration, AC），一种新型的人脸识别校准策略，将归一化嵌入向量间的余弦相似度映射为可信的概率。通过引入局部上下文信息，AC修正了余弦相似度的根本缺陷——相同距离在不同嵌入区域可能对应不同匹配概率。该方法在不依赖人口统计学元数据的前提下，显著提升了整体性能与校准公平性。在多种预训练模型和标准基准测试中，AC 均优于现有方法，在准确率与公平性指标上保持一致领先。该方案实现了连续的、区域特异性的校准，避免了传统方法因追求公平而牺牲部分群体性能的‘向下拉平’现象，为实现公平且高性能的人脸识别提供了实用解决方案。
原文摘要 · Abstract (English)
We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities. By incorporating local context into calibration, Adaptive Calibration corrects for a fundamental mismatch in cosine similarity, whereby the same distance can correspond to different match probabilities in different embedding regions. Our approach improves both overall performance and results in a fairer calibration without requiring demographic metadata. Our approach consistently dominates existing methods both on accuracy and fairness metrics across a variety of pretrained models and standard benchmarks. AC provides a practical solution for equitable facial recognition, without requiring demographic group annotations, and while improving overall performance. Unlike existing approaches, our method provides continuous, region-specific calibration that avoids "leveling down" where fairness comes at the cost of degraded performance for some groups.
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