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 matching tasks such as 1:1 face
verification. We derive and examine the statistical properties of these methods
and demonstrate how coverage and interval width vary with sample size, error
rates, and degree of data dependence using both synthetic and real-world
datasets. Based on our findings, we provide recommendations for best practices
for constructing confidence intervals for error rates in matching tasks.

本文介绍了构建置信区间的方法，以评估匹配算法的误差率，并探讨了样本大小、误差率和数据相关性对覆盖率和区间宽度的影响。研究发现，在匹配任务中构建置信区间的最佳实践包括注意数据原型、选择适当的置信水平以及进行模拟和分析。