The Annals of Applied Statistics

A generalized mixed model framework for assessing fingerprint individuality in presence of varying image quality

Sarat C. Dass, Chae Young Lim, and Tapabrata Maiti

Full-text: Open access

Abstract

Fingerprint individuality refers to the extent of uniqueness of fingerprints and is the main criteria for deciding between a match versus nonmatch in forensic testimony. Often, prints are subject to varying levels of noise, for example, the image quality may be low when a print is lifted from a crime scene. A poor image quality causes human experts as well as automatic systems to make more errors in feature detection by either missing true features or detecting spurious ones. This error lowers the extent to which one can claim individualization of fingerprints that are being matched. The aim of this paper is to quantify the decrease in individualization as image quality degrades based on fingerprint images in real databases. This, in turn, can be used by forensic experts along with their testimony in a court of law. An important practical concern is that the databases used typically consist of a large number of fingerprint images so computational algorithms such as the Gibbs sampler can be extremely slow. We develop algorithms based on the Laplace approximation of the likelihood and infer the unknown parameters based on this approximate likelihood. Two publicly available databases, namely, FVC2002 and FVC2006, are analyzed from which estimates of individuality are obtained. From a statistical perspective, the contribution can be treated as an innovative application of Generalized Linear Mixed Models (GLMMs) to the field of fingerprint-based authentication.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1314-1340.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091215

Digital Object Identifier
doi:10.1214/14-AOAS734

Mathematical Reviews number (MathSciNet)
MR3271334

Zentralblatt MATH identifier
1303.62042

Keywords
Biometric authentication fingerprint-based authentication individuality GLMMs Bayesian inference Laplace approximation

Citation

Dass, Sarat C.; Lim, Chae Young; Maiti, Tapabrata. A generalized mixed model framework for assessing fingerprint individuality in presence of varying image quality. Ann. Appl. Stat. 8 (2014), no. 3, 1314--1340. doi:10.1214/14-AOAS734. https://projecteuclid.org/euclid.aoas/1414091215


Export citation

References

  • Dass, S. (2010). Assessing fingerprint individuality in presence of noisy minutiae. IEEE Trans. of Information Forensics and Security 5 62–70.
  • Dass, S. and Jain, A. K. (2006). Validating a biometric authentication system: Sample size requirements. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 1902–1319.
  • Dass, S. and Li, M. (2009). Hierarchical mixture models for assessing fingerprint individuality. Ann. Appl. Stat. 3 1448–1466.
  • Dass, S. C., Lim, C. and Maiti, T. (2014). Supplement to “A generalized mixed model framework for assessing fingerprint individuality in presence of varying image quality.” DOI:10.1214/14-AOAS734SUPP.
  • Daubert vs. Merrell Dow Pharmaceuticals (1995). 509 U.S. 579, 113 S. Ct. 2786, 125 L.Ed.2d 469.
  • FVC2006: Fingerprint Verification Competition (2006). http://bias.csr.unibo.it/fvc2006/.
  • Home Office Automatic Fingerprint Recognition System (HOAFRS), License 16-93-0026 (1993). Science and technology group. Home Office, London.
  • Lehmann, E. L. and Romano, J. P. (2005). Testing Statistical Hypotheses, 3rd ed. Springer, New York.
  • Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L. and Jain, A. K. (2002). FVC2002: Fingerprint verification competition. In Proceedings of the International Conference on Pattern Recognition (ICPR) 744–747. IEEE Computer Society, Quebec, Canada.
  • National Academy of Sciences Committee on Identifying the Needs of the Forensic Science Community, National Research Council (2009). Strengthening forensic science in the United States: A path forward. National Academies Press.
  • Pankanti, S., Prabhakar, S. and Jain, A. K. (2002). On the individuality of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 1010–1025.
  • Shun, Z. and McCullagh, P. (1995). Laplace approximation of high-dimensional integrals. J. Roy. Statist. Soc. Ser. B 57 749–760.
  • Tabassi, E., Wilson, C. and Watson, C. (2004). Fingerprint image quality. Technical Report 7151. Available at http://fingerprint.nist.gov/NBIS.
  • U.S. vs. Byron C. Mitchell (1999). Criminal Action No. 96-407, U.S. District Court for the Eastern District of Pennsylvania.
  • Zhu, Y., Dass, S. C. and Jain, A. K. (2007). Statistical models for assessing the individuality of fingerprints. IEEE Transactions on Information Forensics and Security 2 391–401.

Supplemental materials

  • Supplementary material: For the supplemental article “A generalized mixed model framework for assessing fingerprint individuality in presence of varying image quality”. The results quoted in the main text are proved in Section 1 and the tables of PRC results for DB3 are in Section 2.