Multiple Factors Based Evaluation of Fingerprint Images Quality

  • Yongming Yang
  • Zulong Zhang
  • Fengling Han
  • Kunming Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7672)


Automatic assessment of Fingerprint Image Quality (FIQ) has significant influence on the performance of Automated Fingerprint Identification Systems (AFISs). Local texture and global texture clarity of fingerprint images are the main factors in the evaluation of FIQ. Available image size, dryness and Singular Points (SPs) of a fingerprint image are also considered as cofactors, each of them has different effect on the assessment of image quality. In this paper, Homogeneous-Zones-Divide is proposed to evaluate the global clarity of a fingerprint image. To be consistent with human perception of fingerprint images quality, the optimal weight is obtained by a constrained nonlinear optimization model. This optimal weight is further used to assess Composite Quality Score (CQS). Simulation on public database indicates that the precision of our method can achieve 97.5% of accurate rate and it can reasonably classify fingerprint images into four grades, which is helpful to improve the performance of (AFIS).


Biometrics Fingerprint image quality (FIQ) Homogeneous-Zones- Divide (HZD) Optimal 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jain, A.K., Karthin, N., Abhishek, N.: Biometric Template Security. EURASIP Journal on Advances in Signal Processing, Article ID 579416 (2008)Google Scholar
  2. 2.
    Umut, U., Sharath, P., Saiil, P., et al.: Biometric Cryptosystems: Issues and Challenges. In: Proc. IEEE (Special Issue on Multimedia Security for Digital Rights Management), vol. 92(6), pp. 948–960 (2004)Google Scholar
  3. 3.
    Jain, A.K., Hong, L., Bolle, R.: On-line Fingerprint Verification. IEEE Trans. Pattern Analysis and Machine Intelligence 19(4), 302–314 (1997)CrossRefGoogle Scholar
  4. 4.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)CrossRefGoogle Scholar
  5. 5.
    Fernando, A.F., Julian, F., Javier, O.G., et al.: A Comparative Study of Fingerprint Image-Quality Estimation Methods. IEEE Trans. Information Forensics and Security 2(4), 734–743 (2007)CrossRefGoogle Scholar
  6. 6.
    Fronthaler, H., Kollreider, K., Bigun, J., Fierrez, J., et al.: Fingerprint Image-Quality Estimation and its Application to Multi Algorithm Verification. IEEE Trans. Information Forensics and Security 3(2), 331–338 (2008)CrossRefGoogle Scholar
  7. 7.
    Lee, S., Choi, H., Choi, K., Kim, J.: Fingerprint-Quality Index using Gradient Components. IEEE Trans. Information Forensics and Security 3(4), 792–800 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wu, J., Xie, S., Seo, D., Lee, W.: A New Approach for Classification of Fingerprint Image Quality. In: Proc. 7th IEEE Int. Conf. Cognitive Informatics (ICCI 2008), pp. 375–383 (2008)Google Scholar
  9. 9.
    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
  10. 10.
    Lee, B., Moon, J., Kim, H.: A Novel Measure of Fingerprint Image Quality using Fourier Spectrum. In: Proc. SPIE, Bellingham, WA, vol. 5779, pp. 105–112 (2005)Google Scholar
  11. 11.
    Yun, E., Cho, S.: Adaptive Fingerprint Image Enhancement with Fingerprint Image Quality Analysis. Image and Vision Computing 24, 101–110 (2006)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Zhao, Y.Y., Cai, A.N.: Fingerprint Image Quality Analysis. Journal of Computer-Aided Design & Computer Graphics 18(5), 644–650 (2006)Google Scholar
  14. 14.
    Ren, Q., Zhang, X.P., Tian, J.: Automatic Assessment of Fingerprint Image Quality. In: 6th Int. Conf. of Youth Computer Worker and the Second Workshop on Biometrics, Hangzhou, China, vol. 4, pp. 14–26 (2001)Google Scholar
  15. 15.
    Lim, E., Jiang, X., Yau, W.: Fingerprint Quality and Validity Analysis. In: IEEE ICIP 2002, NY, USA, vol. I, pp. 469–472 (2002)Google Scholar
  16. 16.
    Zhang, Y., Yin, Y.L., Luo, G.Q.: Quality Classification Method for Fingerprint Image based on Support Vector Machine. Pattern Recognition and Artificial Intelligence 22(1), 129–135 (2009)Google Scholar
  17. 17.
    Tian, J., Yang, X.: Biometrics Theory and Application. Tsinghua University Press, Beijing (2009)Google Scholar
  18. 18.
    Mei, Y., Sun, H.J., Xia, D.S.: Effective Method for Detection of Fingerprints’ Singular Points. Computer Engineering and Applications 44(28), 1–3 (2008)Google Scholar
  19. 19.
    Kass, M., Witkin, A.: Analyzing Oriented Patterns. Computer Vision, Graphics, and Image Processing 37(4), 362–385 (1987)CrossRefGoogle Scholar
  20. 20.
    Maltoni, D., Maio, D., Jain, A.K., et al.: Handbook of Fingerprint Recognition, 2nd edn. Springer, London (2009)CrossRefGoogle Scholar
  21. 21.
    Wang, Y., Hu, J., Phillips, D.: A Fingerprint Orientation Model based on 2D Fourier Expansion (FOMFE) and its Application to Singular-Point Detection and Fingerprint Indexing. IEEE Trans. Pattern Analysis Machine Intelligence 29(4), 573–585 (2007)CrossRefGoogle Scholar
  22. 22.
    Fan, L.L., Wang, S., Wang, H.F., et al.: Singular Points Detection based on Zero-Pole Model in Fingerprint Images. IEEE Trans. Pattern Analysis Machine Intelligence 30(6), 929–940 (2008)CrossRefGoogle Scholar
  23. 23.
    VERIFIER. Neurotechnologija Ltd.,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yongming Yang
    • 1
  • Zulong Zhang
    • 1
  • Fengling Han
    • 2
  • Kunming Lin
    • 1
  1. 1.State Key Laboratory of Power Transmission Equipment & System Security and New TechnologyChongqing UniversityChongqingChina
  2. 2.School of Computer Science and ITRMIT UniversityMelbourneAustralia

Personalised recommendations