Skip to main content

Fingerprint Image Quality

  • Reference work entry

Synonym

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

Definition

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.

Introduction

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 via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   449.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)

    MATH  Google Scholar 

  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. Tabassi, E., Wilson, C., Watson, C.: Fingerprint Image Quality, NFIQ. National Institute of Standards and Technology, NISTIR 7151 edn. (2004)

    Google Scholar 

  4. Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. In: ICIP (2), pp. 37–40 (2005)

    Google Scholar 

  5. Tilton, C., et al.: The BioAPI Specification. American National Standards Institute, Inc. (2002)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Chen, Y., Dass, S.C., Jain, A.K.: Fingerprint quality indices for predicting authentication performance. In: AVBPA, pp. 160–170 (2005)

    Google Scholar 

  10. Shen, L., Kot, A.C., Koo, W.M.: Quality measures of fingerprint images. In: AVBPA, pp. 266–271 (2001)

    Google Scholar 

  11. Ratha, N., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2004)

    Book  Google Scholar 

  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. Chen, T.P., Jiang, X., Yau, W.Y.: Fingerprint image quality analysis. In: ICIP, pp. 1253–1256 (2004)

    Google Scholar 

  14. Bolle, R., et al.: System and methods for determining the quality of fingerprint images. US Patent 596356 (1999)

    Google Scholar 

  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)

    Article  Google Scholar 

  16. Nill, N., Bouzas, B.H.: Objective image quality measure derived from digital image power spectra. Opt. Eng. 31(4), 813–825 (1992)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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 Workshop

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Tabassi, E., Grother, P. (2009). Fingerprint Image Quality. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_52

Download citation

Publish with us

Policies and ethics