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Enhancement of Low Quality Fingerprints Based on Anisotropic Filtering

  • Xinjian Chen
  • Jie Tian
  • Yangyang Zhang
  • Xin Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

The enhancement of the low quality fingerprint is a difficult and challenge task. This paper proposes an efficient algorithm based on anisotropic filtering to enhance the low quality fingerprint. In our algorithm, an orientation filed estimation with feedback method was proposed to compute the accurate fingerprint orientation. The gradient-based approach was firstly used to compute the coarse orientation. Then the reliability of orientation was computed from the gradient image. If the reliability of the estimated orientation is less than pre-specified threshold, the orientation will be corrected by the mixed orientation model. And an anisotropic filtering was used to enhance the fingerprint, with the advantages of its efficient ridge enhancement and its robustness against noise in the fingerprint image. The proposed algorithm has been evaluated on the databases of Fingerprint verification competition (FVC2004). Experimental results confirm that the proposed algorithm is effective and robust for the enhancement of the low quality fingerprint.

Keywords

Gabor Filter Fingerprint Image Feedback Method Fingerprint Recognition Anisotropic Filter 
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.

References

  1. 1.
    Shi, C., Wang, Y.C., Qi, J., Xu, K.: A New Segmentation Algorithm for Low Quality Fingerprint Image. ICIG 2004, 314–317 (2004)Google Scholar
  2. 2.
    Zhou, J., Gu, J.W.: A Model-based Method for the computation of Fingerprints’ Orientation Field. IEEE Trans. On Image Processing 13(6), 821–835 (2004)CrossRefGoogle Scholar
  3. 3.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. PAMI 20(8), 777–789 (1998)Google Scholar
  4. 4.
    Yang, J.W., Liu, L.F., Jiang, T.Z., Fan, Y.: A modified Gabor filter design method for fingerprint image enhancement. Pattern Recognition 24, 1805–1817 (2003)CrossRefGoogle Scholar
  5. 5.
    Willis, A.J., Myers, L.: A Cost-effective Fingerprint Recognition System for Use with Low-quality Prints and Damaged Fingertips. Pattern Recognition 34(2), 255–270 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Sherlock, B., Monro, D.: A Model for Interpreting Fingerprint Topology. Pattern Recognition 26(7), 1047–1095 (1993)CrossRefGoogle Scholar
  7. 7.
    Chen, X.J., Tian, J., Cheng, J.G., Yang, X.: Segmentation of Fingerprint Images Using Linear Classifier. EURASIP Journal on Applied Signal Processing 2004(4), 480–494 (2004)CrossRefGoogle Scholar
  8. 8.
    Yang, G.Z., Burger, P., Firmin, D.N., Underwood, S.R.: Structure Adaptive Anisotropic Filtering. Image and Vision Computing 14, 135–145 (1996)CrossRefGoogle Scholar
  9. 9.
    Biometric Systems Lab, Pattern Recognition and Image Processing Laboratory, Biometric Test Center, http://bias.csr.unibo.it/fvc2004/

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xinjian Chen
    • 1
  • Jie Tian
    • 1
  • Yangyang Zhang
    • 1
  • Xin Yang
    • 1
  1. 1.Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of Sciences, Graduate School of the Chinese Academy of ScienceBeijingChina

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