Abstract
The focus of this chapter is the false non-match rate. The false non-match rate (FNMR) is the rate at which a biometric matcher miscategorizes two signals from the same individual as being from different individuals. In this chapter, we focus on statistical methods for estimation and hypothesis testing of FNMR rates. We start with the notation and the correlation structure that we will use throughout the chapter. We then present statistical methods for estimating and comparing FNMR’s. This includes confidence interval and hypothesis testing for a single FNMR as well as for comparing two or more FNMR’s. These methods are done using both large sample as well as non-parametric methods. A discussion of sample size and power calculation follows that section. We conclude this chapter with a section on prediction intervals and a discussion section.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bailly-Bailliére, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Marièthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruiz, B., Thiran, J.P.: The BANCA database and evaluation protocol. In: 4th International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA. Springer, Berlin (2003)
Bolle, R.M., Ratha, N.K., Pankanti, S.: Error analysis of pattern recognition systems—the subsets bootstrap. Computer Vision and Image Understanding 93, 1–33 (2004)
Crowder, M.J.: Beta-binomial anova for proportions. Applied Statistics 27(1), 34–37 (1978). http://www.jstor.org/stable/2346223
Doddington, G.R., Przybocki, M.A., Martin, A.F., Reynolds, D.A.: The NIST speaker recognition evaluation: overview methodology, systems, results, perspective. Speech Communication 31(2–3), 225–254 (2000)
Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. Wiley, New York (2003)
Garren, S.T., Smith, R.L., Piegorsch, W.W.: Bootstrap goodness-of-fit test for the beta-binomial model. Journal of Applied Statistics 28(5), 561–571 (2001)
Golfarelli, M., Maio, D., Maltoni, D.: On the error-reject trade-off in biometric verification systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 786–796 (1997)
Guyon, I., Makhoul, J., Schwartz, R., Vapnik, V.: What size test set gives good error rate estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 52–64 (1998)
Hall, P.: On the bootstrap and confidence intervals. The Annals of Statistics 14, 1431–1452 (1986)
Mansfield, T., Wayman, J.L.: Best practices in testing and reporting performance of biometric devices. www.cesg.gov.uk/site/ast/biometrics/media/BestPractice.pdf (2002)
Poh, N., Bengio, S.: Database, protocol and tools for evaluating score-level fusion algorithms in biometric authentication. Pattern Recognition Journal (2005)
Poh, N., Martin, A., Bengio, S.: Performance generalization in biometric authentication using joint user-specific and sample bootstraps. IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
Rao, C.R.: Linear Statistical Inference and Its Application, 2nd edn. Wiley–Interscience, New York (2002)
Ridout, M.S., Demétrio, C.G.B., Firth, D.: Estimating intraclass correlation for binary data. Biometrics 55, 137–148 (1999)
Schuckers, M.E.: Estimation and sample size calculations for correlated binary error rates of biometric identification rates. In: Proceedings of the American Statistical Association: Biometrics Section [CD-ROM]. American Statistical Association, Alexandria, VA (2003)
Schuckers, M.E.: Using the beta-binomial distribution to assess performance of a biometric identification device. International Journal of Image and Graphics 3(3), 523–529 (2003)
Schuckers, M.E.: Theoretical statistical correlation for biometric identification performance. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2008)
Schuckers, M.E.: A parametric correlation framework for the statistical evaluation and estimation of biometric-based classification performance in a single environment. IEEE Transactions on Information Forensics and Security 4, 231–241 (2009)
Shen, W., Surette, M., Khanna, R.: Evaluation of automated biometrics-based identification and verification systems. Proceedings of the IEEE 85(9), 1464–1478 (1997)
Wayman, J.L.: Confidence interval and test size estimation for biometric data. In: Proceedings of IEEE AutoID’99, pp. 177–184 (1999)
Williams, D.A.: The analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. Biometrics 31, 949–952 (1975)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer London
About this chapter
Cite this chapter
Schuckers, M.E. (2010). False Non-Match Rate. In: Computational Methods in Biometric Authentication. Information Science and Statistics. Springer, London. https://doi.org/10.1007/978-1-84996-202-5_3
Download citation
DOI: https://doi.org/10.1007/978-1-84996-202-5_3
Publisher Name: Springer, London
Print ISBN: 978-1-84996-201-8
Online ISBN: 978-1-84996-202-5
eBook Packages: Computer ScienceComputer Science (R0)