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Matching algorithms are commonly used to predict matches between items in acollection. For example, in 1:1 face verification, a matching algorithmpredicts whether two face images depict the same person. Accurately assessingthe uncertainty of the error rates of such algorithms can be challenging whendata are dependent and error rates are low, two aspects that have been oftenoverlooked in the literature. In this work, we review methods for constructingconfidence intervals for error rates in 1:1 matching tasks. We derive andexamine the statistical properties of these methods, demonstrating how coverageand interval width vary with sample size, error rates, and degree of datadependence on both analysis and experiments with synthetic and real-worlddatasets. Based on our findings, we provide recommendations for best practicesfor constructing confidence intervals for error rates in 1:1 matching tasks.

Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations