Skip to main content
Log in

Coupling orientation diffusion with coherence-enhancing diffusion: a fingerprint case

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The enhancement of coherent flow-like structures is desired for many image processing tasks, such as segmentation and feature detection. This task can be accomplished in a natural way by adopting anisotropic diffusion filtering using a diffusion matrix adapted to the local structure. This method is referred to as coherence-enhancing diffusion (CED). The performance of CED can be analyzed by observing the evolution of the orientation field (OF) associated with an evolving diffusion matrix. It was revealed from a series of experiments that the final OF from a CED-enhanced image sometimes strays from its true underlying OF (marked by a human expert), degrading its performance. In this paper, a strategy is proposed which repeatedly cleans the OF associated with a diffusion matrix. Thus, for every iteration of CED, its OF is diffused separately until it converges and is then fed back to the CED process to move forward. This hypothesis is tested with the motive of getting an enhanced CED performance. The proposed scheme is validated using fingerprint data, and their numerical results are displayed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Khan, T.M., Bailey, D.G., Khan, M.A.U., Kong, Y.: Efficient hardware implementation strategy for local normalization of fingerprint images. J. Real Time Image Process. (2016). doi:10.1007/s11554-016-0625-8

  2. Zahedi, M., Ghadi, O.R.: Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation. Signal Image Video Process. 9(2), 267–275 (2015)

    Article  Google Scholar 

  3. Weickert, J.: A review of nonlinear diffusion filtering. Scale-Space Theory in Computer Vision 1252, 3–28 (1997)

    Google Scholar 

  4. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  5. Alvarez, L., Lions, P.-L., Morel, J.-M.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(3), 845–866 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  6. Khan, T.M., Bailey, D.G., Khan, M.A.U., Kong, Y.: Efficient hardware implementation for fingerprint image enhancement using anisotropic Gaussian filter. IEEE Trans. Image Process. 26(5), 2116–2126 (2017)

    Article  MathSciNet  Google Scholar 

  7. Khan, M.A.U., Khan, T.M., Kittaneh, O., Kong, Y.: Stopping criterion for anisotropic image diffusion. Optik Int J Light Electron Opt 127(1), 156–160 (2016)

    Article  Google Scholar 

  8. Weickert, J.: Coherence-enhancing diffusion filtering. Int. J. Comput. Vis. 31, 111–127 (1999)

    Article  Google Scholar 

  9. Almansa, A., LIndeberg, T.: Fingerprint enhancement by shape adaption of scale-space operators with automatic scale selection. IEEE Trans. Image Process. 9, 2027–2041 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. Perona, P.: Orientation diffusions. IEEE Trans. Image Process. 7, 457–467 (1998)

    Article  Google Scholar 

  11. Gottschlich, C., Schonlieb, C.-B.: Oriented diffusion filtering for enhancing low-quality fingerprint images. IET Biom. 1, 105–113 (2012)

    Article  Google Scholar 

  12. Weickert, J.: Coherence-enhancing diffusion of colour images. Image Vis. Comput. 17, 201–212 (1999)

    Article  Google Scholar 

  13. Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20, 777–789 (1998)

    Article  Google Scholar 

  14. Jain, A.K., Pankanti, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21, 348–359 (1999)

    Article  Google Scholar 

  15. Sherlock, B.G., Monro, D.M., Millard, K.: Fingerprint enhancement by directional Fourier filtering. IEEE Proc. Vis. Image Signal Process. 141, 87–94 (1994)

    Article  Google Scholar 

  16. Wilson, C.L., Candela, G.T., Watson, C.I.: Neural network fingerprint classification. Artif. Neural Netw. 1, 203–228 (1994)

    Google Scholar 

  17. Truc, P.T.H., Khan, M.A.U., Lee, Y.K., Kim, T.S.: Vessel enhancement filter using directional filter bank. Comput. Vis. Image Underst. 113, 101–112 (2009)

    Article  Google Scholar 

  18. Allen, F.H., Johnson, O.: Automated conformational analysis from crystallographic data. 4. Statistical descriptors for a distribution of torsion angles. Acta Crystallogr. B47, 62–67 (1991)

    Article  Google Scholar 

  19. Yang, Y., Zulong, Z., Lin, K., Han, F.: “A new method of singular points accurate localization for fingerprint,” Physics Procedia, 2012 International Conference on Medical Physics and Biomedical Engineering (ICMPBE2012), vol. 33, pp. 67 – 74 (2012)

  20. Khan, T.M., Khan, M.A.U., Kong, Y.: Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters. Optik Int. J. Light Electron Opt. 25, 4206–4214 (2014)

    Article  Google Scholar 

  21. Khan, M.A.U., Khan, T.M., Halabi, W.A., Shahid, H., Kong, Y.: Coherence enhancement diffusion using robust orientation estimation. IJMA 6, 23–34 (2014)

    Article  Google Scholar 

  22. Julasayvake, A., Choomchuay, S.: An algorithm for fingerprint core point detection. In: International Symposium on Signal Processing and its Applications IAAPA-2007 (2007)

  23. Abraham, J., Kwan, P., Gao, J.: Fingerprint matching using a hybrid shape and orientation descriptor. In: Yang, J. (ed.) State of the art in biometrics, pp. 25–56. InTech (2011). doi:10.5772/19105

  24. Abraham, J.: Matlab code for fingerprint matching algorithm. http://www.mathworks.com/matlabcentral/fileexchange/29280-fingerprint-matching-algorithm-using-shape-context-and-orientation-descriptors (2010)

  25. Khan, M.A.U., Khan, T.M.: Calibrating second-moment matrix for better shape adaptation with bias term from directional filter bank. Signal Image Video Process. (2017). doi:10.1007/s11760-017-1107-6

  26. Khan, T.M., Khan, M.A.U., Kong, Y., Kittaneh, O.: Stopping criterion for linear anisotropic image diffusion: a fingerprint image enhancement case. EURASIP J. Image Video Process. 2016(1), 6 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tariq M. Khan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, M.A.U., Khan, T.M. & Bailey, D.G. Coupling orientation diffusion with coherence-enhancing diffusion: a fingerprint case. SIViP 12, 513–521 (2018). https://doi.org/10.1007/s11760-017-1187-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-017-1187-3

Keywords

Navigation