Image Quality Measures for Fingerprint Image Enhancement

  • Chaohong Wu
  • Sergey Tulyakov
  • Venu Govindaraju
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


Fingerprint image quality is an important factor in the performance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerprint image quality measurement. We propose limited ring-wedge spectral measure to estimate the global fingerprint image features, and inhomogeneity with directional contrast to estimate local fingerprint image features. Experimental results demonstrate the effectiveness of our proposal.


Image Block Equal Error Rate Fingerprint Image Total Error Rate Adaptive Histogram Equalization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, Y., Dass, S., Jain, A.: Fingerprint quality indices for predicting authentication performance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 160–170. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. In: ICIP, pp. 469–472 (2002)Google Scholar
  3. 3.
    Shen, L., Kot, A., Koo, W.: Quality measures of fingerprint images. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 266–271. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Tabassi, E., Wilson, C.L.: A new approach to fingerprint image quality. In: ICIP, pp. 37–40 (2005)Google Scholar
  5. 5.
    Uchida, K.: Image-based approach to fingerprint acceptability assessment. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 294–300. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transaction on Pattern Recognition and Machine Intelligence 20, 777–789 (1998)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River (2002)Google Scholar
  8. 8.
    Candela, G.T., Grother, P.J., Watson, C.I., Wilkinson, R.A., Wilson, C.L.: Pcasys - a pattern-level classification automation system for fingerprints. Technical Report NISTIR 5647 (1995)Google Scholar
  9. 9.
    Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization. Academic Press, London (1994)Google Scholar
  10. 10.
    Jea, T.Y., Chavan, V.S., Govindaraju, V., Schneider, J.K.: Security and matching of partial fingerprint recognition systems. In: SPIE Defense and Security Symposium (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chaohong Wu
    • 1
  • Sergey Tulyakov
    • 1
  • Venu Govindaraju
    • 1
  1. 1.Center for Unified Biometrics and Sensors (CUBS), SUNY at BuffaloUSA

Personalised recommendations