Fusion Method of Fingerprint Quality Evaluation: From the Local Gabor Feature to the Global Spatial-Frequency Structures

  • Decong Yu
  • Lihong Ma
  • Hanqing Lu
  • Zhiqing Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


We propose a new fusion method to evaluate fingerprint quality by combining both spatial and frequency features of a fingerprint image. In frequency domain, a ring structure of DFT magnitude and directional Gabor features are applied. In spatial domain, black pixel ratio of central area is taken into account. These three features are the most efficient indexes for fingerprint quality assessment. Though additional features could be introduced, their slight improvement in performance will be traded off with complexity and computational load to some extent. Thus in this paper, each of the three features are first employed to assess fingerprint quality, their evaluation performance are also discussed. Then the suggested fusion approach of the three features is presented to obtain the final quality scores. We test the fusion method in our public security fingerprint database. Experimental results demonstrate that the proposed scheme can estimate the quality of fingerprint images accurately. It provides a feasible rejection of poor fingerprint images before they are presented to the fingerprint recognition system for feature extraction and matching.


Quality Score Fusion Method Fingerprint Image Gabor Feature Core Point 
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  1. 1.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Analysis Machine Intelligent 20(8), 777–789 (1998)CrossRefGoogle Scholar
  2. 2.
    Ratha, N.K., Bolle, R.: Fingerprint image quality estimation. In: ACCV, pp. 819–823 (2000)Google Scholar
  3. 3.
    Shen, L.L., Kot, A., Koo, W.M.: Quality measure of fingerprint images. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 266–271. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. In: IEEE ICIP (2002)Google Scholar
  5. 5.
    Lee, B., Moon, J., Kim, H.: A novel measure of fingerprint image quality using Fourier spectrum. In: Proc. SPIE, vol. 5779, p. 105 (2005)Google Scholar
  6. 6.
    Qi, J., Shi, Z., Zhao, X., Wang, Y.: Measuring fingerprint image quality using gradient. In: Proc. SPIE, vol. 5779, p. 455 (2005)Google Scholar
  7. 7.
    Qi, J., Abdurrachim, D., Li, D., Kunieda, H.: A hybrid method for fingerprint image quality calculation. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies, October 17-18, pp. 124–129 (2005)Google Scholar
  8. 8.
    Jain, A.K., Prabhakar, S., Lin, H.: A Multichannel Approach to Fingerprint Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 21(4), 348–359 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Decong Yu
    • 1
  • Lihong Ma
    • 1
    • 2
  • Hanqing Lu
    • 2
  • Zhiqing Chen
    • 3
  1. 1.GD Key Lab. of Computer Network, Dept. of Electronic EngineeringSouth China Univ., of Tech.GuangzhouChina
  2. 2.National Lab of Pattern Recognition, Inst. AutomationChinese Academy of ScienceBeijingChina
  3. 3.Dept. of Public Security of Guangdong ProvinceCriminal Tech. CenterChina

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