Significance test in operational ROC analysis
Jin Chu Wu, Alvin F. Martín, Raghu N. Kacker, Charles R. Hagwood · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
To evaluate the performance of fingerprint-image matching algorithms on large datasets, a receiver operating characteristic (ROC) curve is applied. From the operational perspective, the true accept rate (TAR) of the genuine scores at a specified false accept rate (FAR) of the impostor scores and/or the equal error rate (EER) are often employed. Using the standard errors of these metrics computed using the nonparametric two-sample bootstrap based on our studies of bootstrap variability on large fingerprint datasets, the significance test is performed to determine whether the difference between the performance of one algorithm and a hypothesized value, or the difference between the performances of two algorithms where the correlation is taken into account is statistically significant. In the case that the alternative hypothesis is accepted, the sign of the difference is employed to determine which is better than the other. Examples are provided.