Fingerprint Reconstruction: From Minutiae

  • B. AmminaiduEmail author
  • V. Sreerama Murithy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Fingerprint is one of the very important biometric features used to identify humans across the globe. Minutiae based representation is the most widely used fingerprint representation scheme among other schemes available. Since minutiae representation is a compacted one, there has been an impression that the minutiae template does not contain sufficient information to reconstruct the original grayscale fingerprint image. This misconception has been proven to be incorrect, several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. But all these algorithms have one common drawback that many spurious minutiae, which are not included in the original minutiae template are generated in the reconstructed image. Moreover, some of these techniques can only reconstruct a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed, which not only reconstructs the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. The proposed algorithm reconstructs the continuous phase from minutiae. Experimental results have shown that the proposed algorithm reconstructs whole fingerprint. It also contains very few spurious minutiae.


Fingerprint fingerprint reconstruction phase image minutiae orientation field 


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  1. 1.
    Bazen, A.M., Verwaaijen, G.T.B., Gerez, S.H., Veelenturf, P.J., van der Zwaag, B.J.: A Correlation-Based Fingerprint Verification System. In: Proc. 11th Ann. Workshop Circuits Systems and Signal Processing, pp. 205–213 (November 2000)Google Scholar
  2. 2.
    Thebaud, L.R.: Systems and Methods with Identity Verification by Comparison and Interpretation of Skin Patterns Such as Fingerprints. US Patent No. 5,909,501 (1999)Google Scholar
  3. 3.
    Feng, J., Ouyang, Z., Cai, A.: Fingerprint Matching Using Ridges. Pattern Recognition 39(11), 2131–2140 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hara, M., Toyama, H.: Method and Apparatus for Matching Streaked Pattern Image. US Patent No. 7,295,688 (2007)Google Scholar
  5. 5.
    Ratha, N.K., Bolle, R.M., Pandit, V.D., Vaish, V.: Robust Fingerprint Authentication Using Local Structural Similarity. In: Proc. Fifth IEEE Workshop Applications of Computer Vision, pp. 29–34 (2000)Google Scholar
  6. 6.
    Bazen, A.M., Gerez, S.H.: Fingerprint Matching by Thin-Plate Spline Modelling of Elastic Deformations. Pattern Recognition 36(8), 1859–1867 (2003)CrossRefGoogle Scholar
  7. 7.
    Lee, H.C., Gaensslen, R.E. (eds.): Advances in Fingerprint Technology. Elsevier, New York (1999)Google Scholar
  8. 8.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Hand book of Fingerprint Recognition. Springer (2003)Google Scholar
  9. 9.
    Feng, J., Jain, A.K.: Fingerprint Reconstruction: From Minutiae to Phase. IEEE Transactions on Pattern Analysis And Machine Intelligence 33(2) (February 2011)Google Scholar
  10. 10.
    Feng, J., Jain, A.K.: FM Model Based Fingerprint Reconstruction from Minutiae Template. In: Proc. Second Int’l Conf. Biometrics, pp. 544–553 (June 2009)Google Scholar
  11. 11.
    Larkin, K.G., Fletcher, P.A.: A Coherent Framework for Fingerprint Analysis: are Fingerprints Holograms? Optics Express 15(14), 8667–8677 (2007)CrossRefGoogle Scholar
  12. 12.
    Ghiglia, D.C., Pritt, M.D.: Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. John Wiley and Sons, NewYork (1998)zbMATHGoogle Scholar
  13. 13.
    Ross, A., Shah, J., Jain, A.K.: From template to image: Reconstructing Fingerprints from Minutiae Points. IEEE Trans. Pattern Analysis and Machine Intelligence 29(4), 544–560 (2007)CrossRefGoogle Scholar
  14. 14.
    Cappelli, R., Lumini, A., Maio, D., Maltoni, D.: Fingerprint Image Reconstruction from Standard Template. IEEE Trans. Pattern Analysis and Machine Intelligence 29(9), 1489–1503 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringGMR Institute of TechnologyRajamIndia

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