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Publication Citation: Significance Test in Operational ROC Analysis

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Author(s): Jin Chu Wu; Alvin Martin; Raghu N. Kacker; Robert C. Hagwood;
Title: Significance Test in Operational ROC Analysis
Published: April 05, 2010
Abstract: 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.
Conference: Biometric Technology for Human Identification VII
Proceedings: SPIE Biometrics 2010
Volume: 7667
Pages: pp. 76670I-1 - 76670I-15
Location: Orlando, FL
Dates: April 5-9, 2010
Keywords: Receiver operating characteristic (ROC) curve; Fingerprint; Biometrics; Nonparametric bootstrap; Standard error; Confidence interval; Significance test; Comparison
Research Areas: Biometrics
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