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Hypothesis Test of Fingerprint-Image Matching Algorithms in Operational ROC Analysis

Published

Author(s)

Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker

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 is usually employed. And the equal error rate (EER) can also be used. The accuracies of the measurement TAR and EER in terms of standard errors and 95% confidence intervals can be computed using the nonparametric two-sample bootstrap based on our studies of bootstrap variability on large fingerprint datasets. In this article, the hypothesis testing 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.
Citation
NIST Interagency/Internal Report (NISTIR) - 7586
Report Number
7586

Keywords

Receiver operating characteristic (ROC) curve, Fingerprint, Biometrics, Nonparametric bootstrap, Standard errors, Confidence interval, Significance test, Comparisons

Citation

, J. , Martin, A. and Kacker, R. (2009), Hypothesis Test of Fingerprint-Image Matching Algorithms in Operational ROC Analysis, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=902360 (Accessed June 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 17, 2009, Updated February 19, 2017