Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fingerprint Image Quality

  • Elham Tabassi
  • Patrick Grother
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_52


Expected performance or utility of fingerprint image in an automated comparison environment


The intrinsic characteristic of a biometric signal may be used to determine its suitability for further processing by the biometric system or assess its conformance to preestablished standards. The quality of a biometric signal is a numerical value (or a vector) that measures this intrinsic attribute. Quality score is a quantitative expression of the utility, or predicted performance of a biometric sample in a comparison environment. This means that finger image quality scores should correlate to the observed false match and false non-match rates of the samples.


With an increase in the need for reliable identity authentication, biometric recognition systems have been increasingly deployed in several different applications: government applications such as national ID card, border control; and commercial applications, such as physical access control, e-commerce, or...

This is a preview of subscription content, log in to check access.


  1. 1.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)MATHGoogle Scholar
  2. 2.
    Grother, P., et al.: MINEX: Performance and Interoperability of the INCITS 378 Fingerprint Template. National Institute of Standards and Technology, NISTIR 7296 edn. (2005). http://fingerprint.nist.gov/minex04
  3. 3.
    Tabassi, E., Wilson, C., Watson, C.: Fingerprint Image Quality, NFIQ. National Institute of Standards and Technology, NISTIR 7151 edn. (2004)Google Scholar
  4. 4.
    Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. In: ICIP (2), pp. 37–40 (2005)Google Scholar
  5. 5.
    Tilton, C., et al.: The BioAPI Specification. American National Standards Institute, Inc. (2002)Google Scholar
  6. 6.
    Benini, D., et al.: ISO/IEC 29794-1 Biometric Sample Quality Standard: Framework. JTC1 / SC37 / Working Group 3 (2008). http://isotc.iso.org/isotcportal
  7. 7.
    Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Fronthaler, H., Kollreider, K., Bigun, J.: A Comparative study of fingerprint image-quality estimation methods. IEEE Trans. Inform. Forens. Secur. 2, 734–743 (2007)CrossRefGoogle Scholar
  8. 8.
    Lim, E., Jiang, X., Yau, W.: Fingerprint image quality and validity analysis. In: IEEE proceedings of International Conference on Image Processing (ICIP), pp. 469–472. New York, USA (2002)Google Scholar
  9. 9.
    Chen, Y., Dass, S.C., Jain, A.K.: Fingerprint quality indices for predicting authentication performance. In: AVBPA, pp. 160–170 (2005)Google Scholar
  10. 10.
    Shen, L., Kot, A.C., Koo, W.M.: Quality measures of fingerprint images. In: AVBPA, pp. 266–271 (2001)Google Scholar
  11. 11.
    Ratha, N., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2004)CrossRefGoogle Scholar
  12. 12.
    Lim, E., Toh, K.A., Saganthan, P.N., Jiang, X., Yau, W.Y.: Fingerprint image quality analysis. In: ICIP, pp. 1241–1244 (2004)Google Scholar
  13. 13.
    Chen, T.P., Jiang, X., Yau, W.Y.: Fingerprint image quality analysis. In: ICIP, pp. 1253–1256 (2004)Google Scholar
  14. 14.
    Bolle, R., et al.: System and methods for determining the quality of fingerprint images. US Patent 596356 (1999)Google Scholar
  15. 15.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Analy. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  16. 16.
    Nill, N., Bouzas, B.H.: Objective image quality measure derived from digital image power spectra. Opt. Eng. 31(4), 813–825 (1992)CrossRefGoogle Scholar
  17. 17.
    National Institute of Standards and Technology: NIST Biometric Image Software (NBIS) (2008). http://www.itl.nist.gov/iad/894.03/nigos/nbis.html
  18. 18.
    Ko, T., Krishnan, R.: Monitoring and reporting of fingerprint image quality and match accuracy for a large user application. In: Proceedings of the 33rd Applied Image Pattern Recognition Workshop, pp. 159–164. IEEE Computer Society (2004)Google Scholar
  19. 19.
    Tabassi, E., Grother, P.: Quality Summarization: Recommendations on Enterprise-wide Biometric Quality Summarization. National Institute of Standards and Technology, NISTIR 7244 edn. (2007)Google Scholar
  20. 20.
    Wein, L.M., Baveja, M.: Using fingerprint image quality to improve the identification performance of the u.s. visit program. In: Proceedings of the National Academy of sciences (2005). www.pnas.org/cgi/doi/10.1073/pnas.0407496102
  21. 21.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Bigun, J.: Discriminative multimodal biometric authentication based on quality measures. Pattern Recogn. 38(5), 777–779 (2005)CrossRefGoogle Scholar
  22. 22.
    Tabassi, E., Quinn, G.W., Grother, P.: When to fuse two biometrics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR-06. New York (2006). Biometric WorkshopGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Elham Tabassi
    • 1
  • Patrick Grother
    • 1
  1. 1.National Institute of Standards and TechnologyMDUSA