Quality Assessment Based Fingerprint Segmentation

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


Lack of robust segmentation against degraded quality image is one of the open issues in fingerprint segmentation. Good fingerprint segmentation effectively reduces the processing time in automatic fingerprint recognition systems. Poor segmentation result in spurious and missing features thus degrading performance of overall system. Segmentation will be more effective if done in accordance to the quality of image. Fingerprint images with high quality have wide range of features which can be used for segmentation than the low quality image, where the fingerprint features are not clearly visible. This paper focus on the two folded segmentation process comprising of quality evaluation and segmentation based on it. Various global and local features are used for assessing quality of image and thereby using them for segmenting ridge area from plain background. The segmented images are compared using percentage of foreground area to total area, genuine number of minutiae points extracted from segmented area. The time taken for image segmentation is also used as a performance parameter. The proposed approach has been tested with images of different qualities from NIST and FVC data sets and the results are proven to be better than the conventional segmentation approaches.


OCL (Orientation Certainty Level) Quality Index CM (Consistency Measure) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer (June 2003)Google Scholar
  2. 2.
    U. I. A. of India, (last accessed on December 25, 2011)
  3. 3.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Dass, S.C., Jain, A.K.: 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
  5. 5.
    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 Information Forensics and Security 2, 734–743 (2007)CrossRefGoogle Scholar
  6. 6.
    Simon-Zorita, D., Ortega-Garcia, J., et al.: Image quality and position varia-bility assessment in minutiae-based fingerprint verification. Proc. Inst. Elect. Eng., Vis. Image Signal Process. 150(6), 402–408 (2003)CrossRefGoogle Scholar
  7. 7.
    Fiérrez-Aguilar, J., Chen, Y., Ortega-Garcia, J., Jain, A.K.: Incorporating Image Quality in Multi-algorithm Fingerprint Verification. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 213–220. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Chen, Y., Dass, S.C., Jain, A.K.: 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
  9. 9.
    Otsu, N.: A threshold selection method from gray level histogram. IEEE Trans. Syst. Man Cybern. SMC-9, 62–66 (1979)Google Scholar
  10. 10.
    Helfroush, M., Mohammadpour, M.: Fingerprint segmentation. presented at 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria (2008)Google Scholar
  11. 11.
    Joun, S., Kim, H., Chung, Y., Ahn, D.: An experimental study on measur-ing image quality of infant fingerprints. In: Proc. KES 2003, pp. 1261–1269 (2003)Google Scholar
  12. 12.
    Ko, T., Krishnan, R.: Monitoring and reporting of fingerprint image quality and match accuracy for a large user application. In: Proc. AIPR 2004, pp. 159–164 (2004)Google Scholar
  13. 13.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Target de-pendent score normalization techniques and their application to signature verification. IEEE Trans. Syst. Man. Cybern. C, Appl. Rev. 35(3), 418–425 (2005)CrossRefGoogle Scholar
  14. 14.
    Lee, S., Choi, H., Choi, K., Kim, J.: Finger-print-Quality Index Using Gradient Components. IEEE Transactions on Information Forensics and Security 3(4) (December 2008)Google Scholar
  15. 15.
    Tabassi, E., Wilson, C., Watson, C.: Fingerprint image quality. NIST. Res. Rep. NISTIR7151 (August 2004)Google Scholar
  16. 16.
    Xie, S.J., Yang, J.C., Yoon, S., Park, D.S.: An Optimal Orientation Certainty Level Approach for Fingerprint Quality Estimation. In: Second International Symposium on Intelligent Information Technology Application, vol. 3, pp. 722–726 (2008)Google Scholar
  17. 17.
    Turroni, F., Maltoni, D., Cappelli, R., Member, D.M.: Improving Fingerprint Orientation Extraction. IEEE Transactions on Information Forensics and Security 6(3) (September 2011)Google Scholar
  18. 18.
    Wang, S., Zhang, W., Wang, Y.: New features Extraction and Application in Fingerprint segmentation (2002)Google Scholar
  19. 19.
    Saquib, Z., Soni, S.K., Vij, R.: 2010 International Conference on Computer Design And Appliations, ICCDA 2010. IEEE (2010)Google Scholar
  20. 20.
    Drahanský, M.: Realization of Experiments with Image Quality of Fingerprints. International Journal of Advanced Science and Technology 6 (May 2009)Google Scholar
  21. 21.
    Lim, E., Toh, K.A., Suganthan, P.N., Jiang, X.D., Yau, W.Y.: Fingerprint image quality analysis. In: ICIP 2004, vol. 2, pp. 1241–1244 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Inderprastha Engg. CollegeGhaziabadIndia
  2. 2.IMTGhaziabadIndia

Personalised recommendations