Matching algorithms are commonly used to predict matches between items in a collection. For example, in 1:1 face verification, a matching algorithm predicts whether two face images depict the same person. Accurately assessing the uncertainty of the error rates of such algorithms can be challenging when data are dependent and error rates are low, two aspects that have been often overlooked in the literature. In this work, we review methods for constructing confidence intervals for error rates in 1:1 matching tasks. We derive and examine the statistical properties of these methods, demonstrating how coverage and interval width vary with sample size, error rates, and degree of data dependence on both analysis and experiments with synthetic and real-world datasets. Based on our findings, we provide recommendations for best practices for constructing confidence intervals for error rates in 1:1 matching tasks.

翻译：匹配算法常用于预测集合中项目之间的匹配关系。例如，在1:1人脸验证中，匹配算法会预测两张人脸图像是否属于同一人。当数据存在依赖关系且错误率较低时，准确评估此类算法错误率的不确定性具有挑战性——这两个方面在文献中常被忽视。本研究系统回顾了1:1匹配任务中构建错误率置信区间的方法。我们推导并检验了这些方法的统计特性，通过理论分析与合成数据、真实数据集的实验，揭示了置信区间覆盖率和宽度如何随样本量、错误率及数据依赖程度变化。基于研究发现，我们为1:1匹配任务中构建错误率置信区间提供了最佳实践建议。