Fingerprint Image Enhancement Using Steerable Filter in Wavelet Domain

  • K. S. Jeyalakshmi
  • T. Kathirvalavakumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


The proposed work is to enhance the features of the fingerprint image using steerable filter in wavelet domain to increase the accuracy and speed of Automatic fingerprint identification system. The proposed method uses steerable filter and wavelet. The steerable filter allows filtering process adaptively to any orientation and determining analytically the filter output as a function of orientation and the wavelet domain speeds up the computation process. The steerable filter is applied on each local blocks of approximation image of wavelet transform for tuning up the fingerprint image features and then smoothing the resultant which leads to enhanced image. Experiments are conducted on FVC databases and results show that enhancement process reveals clear visualization of fingerprint images.


Fingerprint enhancement Wavelet transform Steerable filter Orientation field Principal component analysis Multi-scale pyramid decomposition 



This work is funded by University Grants Commission Major Research Project (MRP: F.No. 42-144/2013(SR)), New Delhi, INDIA


  1. 1.
    Blotta, E., Moler, E.: Fingerprint image enhancement by differential hysteresis processing. Forensic Sci. Int. 141(2), 109–113 (2004)CrossRefGoogle Scholar
  2. 2.
    Cavusoglu, A., Gorgunoglu, S.: A fast fingerprint image enhancement algorithm using a parabolic mask. Comput. Electr. Eng. 34(3), 250–256 (2008)CrossRefzbMATHGoogle Scholar
  3. 3.
    Cheng, J., Tian, J.: Fingerprint enhancement with dyadic scale-space. Pattern Recognit. Lett. 25(11), 1273–1284 (2004)CrossRefGoogle Scholar
  4. 4.
    Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint enhancement using STFT analysis. Pattern Recognit. 40(1), 198–211 (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Feng, X.G., Milanfar, P.: Multiscale principal components analysis for image local orientation estimation. IEEE Signals Syst. Comput. 1, 478–482 (2002)Google Scholar
  6. 6.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filter. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn, p. 717. Pearson Education India, New Jersey (2009). Image AnalysisGoogle Scholar
  8. 8.
    Gottschlich, C.: Curved-region-based ridge frequency estimation and curved Gabor filters for fingerprint image enhancement. IEEE Trans. Image Process. 21(4), 2220–2227 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  10. 10.
    Hsieh, C.-T., Lai, E., Wang, Y.-C.: An effective algorithm for fingerprint image enhancement based on wavelet transform. Pattern Recognit. 36(2), 303–312 (2003)CrossRefGoogle Scholar
  11. 11.
    Yang, J., Liu, L., Jiang, T., Fan, Y.: A modified Gabor filter design method for fingerprint image enhancement. Pattern Recognit. Lett. 24(12), 1805–1817 (2003)CrossRefGoogle Scholar
  12. 12.
    Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition, p. 103. Springer Science & Business Media, London (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Paul, A.M., Lourde, R.M.: A study on image enhancement techniques for fingerprint identification. In: IEEE International Conference on Video and Signal Based Surveillance, p. 16 (2006)Google Scholar
  14. 14.
    Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint image. Pattern Recognit. 28(11), 1657–1672 (1995)CrossRefGoogle Scholar
  15. 15.
    Shi, Z., Govindaraju, V.: A chaincode based scheme for fingerprint feature extraction. Pattern Recogni. Lett. 27(5), 462–468 (2006)CrossRefGoogle Scholar
  16. 16.
    Wang, W., Li, J., Huang, F., Feng, H.: Design and implementation of Log-Gabor filter in fingerprint image enhancement. Pattern Recognit. Lett. 29(3), 301–308 (2008)CrossRefGoogle Scholar
  17. 17.
    Wu, C., Tulyakov, S., Govindaraju, V.: Robust point-based feature fingerprint segmentation algorithm. In: Lee, S.-W., Li, S.Z. (eds.) International Conference on Biometrics. LNCS, vol. 4642, pp. 1095–1103. Springer, Heidelberg (2007)Google Scholar
  18. 18.
    Wu, C., Shi, Z., Govindaraju, V.: Fingerprint image enhancement method using directional median filter. In: Proceedings of SPIE, vol. 5404, p. 67 (2004)Google Scholar
  19. 19.
    Yang, J., Liu, L., Jiang, T., Fan, Y.: An effective algorithm for fingerprint image enhancement based on wavelet transform. Pattern Recognit. 36(2), 303–312 (2003)CrossRefGoogle Scholar
  20. 20.
    Yang, J., Xiong, N., Vasilakos, A.V.: Two-stage enhancement scheme for low quality fingerprint images by learning from the images. IEEE Trans. Hum. Mach. Syst. 43(2), 235–248 (2013)CrossRefGoogle Scholar
  21. 21.
    Ye, Q., Xiang, M., Cui, Z.: Fingerprint image enhancement algorithm based on two dimension EMD and Gabor filter. Procedia Eng. 29, 1840–1844 (2012)CrossRefGoogle Scholar
  22. 22.
    Zhang, W., Tang, Y.Y., You, X.: Fingerprint enhancement using wavelet transform combined with Gabor filter. Int. J. Pattern Recognit. Artif. Intell. 18(8), 1391–1406 (2004)CrossRefGoogle Scholar
  23. 23.
  24. 24.
  25. 25.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceN.M.S.S.Vellaichamy Nadar College (Autonomous)MaduraiIndia
  2. 2.Research center in Computer ScienceV.H.N.S.N. College (Autonomous)VirudhunagarIndia

Personalised recommendations