Fingerprint Identification Using Hierarchical Matching and Topological Structures

  • Meryam Elmouhtadi
  • Sanaa El fkihi
  • Driss Aboutajdine
Part of the Studies in Computational Intelligence book series (SCI, volume 730)


Fingerprint identification is one of the most popular and efficient biometric techniques used for improving automatic personal identification. In this paper, we will present a new indexing method, based on estimation of singular point considered as an important feature in the fingerprint by using the directional file. On the other hand, a hierarchical Delaunay triangulation is applied on the minutiae around the extracted singular point. Two fingerprints calculated by introducing the barycenter notion to ensure the exact location of the similar triangles is compared. We have performed extensive experiments and comparisons to demonstrate the effectiveness of the proposed approach using a challenging public database (i.e., FVC2000), which contains small area and low-quality fingerprints.


Fingerprint indexing Delaunay triangulation Barycentre Singular point 


  1. 1.
    Hassanien, A.E.: Hiding iris data for authentication of digital images using wavelet theory. Int. J. Pattern Recogn. Image Anal. 16(4), 637–643 (2006)CrossRefGoogle Scholar
  2. 2.
    Hassanien, A.E.: A copyright protection using watermarking algorithm. Informatica 17(2), 187–198 (2006)MathSciNetMATHGoogle Scholar
  3. 3.
    Kumar, D.A., Begum, T.U.S.: A comparative study on fingerprint matching algorithms for EVM. J. Comput. Sci. Appl. 1(4), 55–60 (2013)Google Scholar
  4. 4.
    Jain, A., Pankanti, S.: Fingerprint classification and matching. in Handbook for Image and Video Processing. Academic Press (2000)Google Scholar
  5. 5.
    de Boer, J., Bazen, A.M., Gerez, S.H.: Indexing fingerprint databases based on multiple features (2001)Google Scholar
  6. 6.
    Zaeri, N.: Minutiae-based fingerprint extraction and recognition, biometrics. In: Yang, J. (ed.) InTech, (2011). doi: 10.5772/17527,
  7. 7.
    U.S.F.B. of Investigation: The science of fingerprints: classification and uses. United States Department of Justice, Federal Bureau of Investigation (1979)Google Scholar
  8. 8.
    Saleh, A., Wahdan, A., Bahaa, A.: Fingerprint recognition. INTECH Open Access Publisher (2011)Google Scholar
  9. 9.
    Ratha, N.K., Karu, K., Chen, S., Jain, A.K.: A real-time matching system for large fingerprint databases. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 799–813 (1996)Google Scholar
  10. 10.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)CrossRefGoogle Scholar
  11. 11.
    Parker, J.: Algorithms for Image Processing and Computer Vision, ser. IT Pro. Wiley (2010)Google Scholar
  12. 12.
    Tisse, C.-L., Martin, L.,Torres, L., Robert, M.: Systèm automatique de reconnaissance d’empreintes digitales sécurisation de l’authentification sur carte à puce (2001)Google Scholar
  13. 13.
    Muñoz-Briseño, A., Alonso, A.G., Palancar, J.H.: Fingerprint indexing with bad quality areas. Expert Syst. Appl. 40(5), 1839–1846 (2013)CrossRefGoogle Scholar
  14. 14.
    Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 905–919 (2002)Google Scholar
  15. 15.
    Zhou, J., Chen, F., Gu, J.: A novel algorithm for detecting singular points from fingerprint images. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1239–1250 (2009)Google Scholar
  16. 16.
    Liu, M., Jiang, X., Kot, A.C.: Efficient fingerprint search based on database clustering”. Pattern Recogn. 40(6), 1793–1803 (2007)CrossRefMATHGoogle Scholar
  17. 17.
    Liu, M.: Fingerprint classification based on Adaboost learning from singularity features. Pattern Recogn. 43(3), 1062–1070 (2010)CrossRefMATHGoogle Scholar
  18. 18.
    Awad, A.I., Baba, K.: Singular point detection for efficient fingerprint classification. Int. J. New Comput. Architect. Appl. (IJNCAA) 2(1), 1–7 (2012)Google Scholar
  19. 19.
    Munoz-Briseno., Alonso, A.G., Palancar, J.H.: Fingerprint indexing with bad quality areas. Expert Syst. Appl. 40(5), 1839–1846 (2013) Google Scholar
  20. 20.
    Elmouhtadi, M., Aboutajdine, D., El Fkihi, S.: Fingerprint indexing based barycenter triangulation. In: 2015 Third World Conference on Complex Systems (WCCS), Marrakech, pp. 1–6 (2015)Google Scholar
  21. 21.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 402–412 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Meryam Elmouhtadi
    • 1
  • Sanaa El fkihi
    • 1
    • 2
  • Driss Aboutajdine
    • 1
  1. 1.Faculty of Science, LRIT – CNRST URAC29University Mohammed V RabatRabatMorocco
  2. 2.RIITM, ENSIASUniversity Mohammed VRabatMorocco

Personalised recommendations