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Fingerprint Identification Based on Hierarchical Triangulation

  • Elmouhtadi, Meryam (Dept. of Physics, LRIT - CNRST URAC29, Faculty of Sciences, Mohammed V University in Rabat) ;
  • El Fkihi, Sanaa (IRDA, ENSIAS, Mohammed V University of Rabat) ;
  • Aboutajdine, Driss (Dept. of Physics, LRIT - CNRST URAC29, Faculty of Sciences, Mohammed V University in Rabat)
  • Accepted : 2017.05.09
  • Published : 2018.04.30

Abstract

Fingerprint-based biometric identification is one of the most interesting automatic systems for identifying individuals. Owing to the poor sensing environment and poor quality of skin, biometrics remains a challenging problem. The main contribution of this paper is to propose a new approach to recognizing a person's fingerprint using the fingerprint's local characteristics. The proposed approach introduces the barycenter notion applied to triangles formed by the Delaunay triangulation once the extraction of minutiae is achieved. This ensures the exact location of similar triangles generated by the Delaunay triangulation in the recognition process. The results of an experiment conducted on a challenging public database (i.e., FVC2004) show significant improvement with regard to fingerprint identification compared to simple Delaunay triangulation, and the obtained results are very encouraging.

Keywords

References

  1. A. Jain and S. Pankanti, "Fingerprint classification and matching," in Handbook for Image and Video Processing. San Diego, CA: Academic Press, 2000, pp. 821-836.
  2. D. A. Kumar and T. U. S. Begum, "A comparative study on fingerprint matching algorithms for EVM," Journal of Computer Sciences and Applications, vol. 1, no. 4, pp. 55-60, 2013. https://doi.org/10.12691/jcsa-1-4-1
  3. J. Feng and J. Zhou, "A performance evaluation of fingerprint minutia descriptors," in Proceedings of 2011 International Conference on Hand-Based Biometrics, Hong Kong, China, 2011, pp. 1-6.
  4. Federal Bureau of Investigation, The Science of Fingerprints: Classification and Uses. Washington, DC: Federal Bureau of Investigation, 2006.
  5. M. Liu, X. Jiang, and A. C. Kot, "Efficient fingerprint search based on database clustering," Pattern Recognition, vol. 40, no. 6, pp. 1793-1803, 2007. https://doi.org/10.1016/j.patcog.2006.11.007
  6. N. K. Ratha, K. Karu, S. Chen, and A. K. Jain, "A real-time matching system for large fingerprint databases," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 799-813, 1996. https://doi.org/10.1109/34.531800
  7. R. Kumar, P. Chandra, and M. Hanmandlu, "A robust fingerprint matching system using orientation features," Journal of Information Processing Systems, vol. 12, no. 1, pp. 83-99, 2016. https://doi.org/10.3745/JIPS.02.0020
  8. J. de Boer, A. M. Bazen, and S. H. Gerez, "Indexing fingerprint databases based on multiple features," in Proceedings of the 12th Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, 2001, pp. 300-306.
  9. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-166, 2004. https://doi.org/10.1117/1.1631315
  10. J. R. Parker, Algorithms for Image Processing and Computer Vision. Hoboken, NJ: John Wiley & Sons, 2010.
  11. C. L. Tisse, L. Martin, L. Torres, and M. Robert, "Systeme automatique de reconnaissance d'empreintes digitales. Securisation de l'authentification sur carte a puce," in $18^{\circ}$ Colloque sur le traitement du signal et des images. Paris, France: Groupe d'Etudes du Traitement du Signal et des Images, 2001, pp. 44-47.
  12. A. Munoz-Briseno, A. Gago-Alonso, and J. HernaNdez-Palancar, "Fingerprint indexing with bad quality areas," Expert Systems with Applications, vol. 40, no. 5, pp. 1839-1846, 2013. https://doi.org/10.1016/j.eswa.2012.09.018
  13. N. Liu, Y. Yin, and H. Zhang, "A fingerprint matching algorithm based on Delaunay triangulation net," in The Proceedings of the 5th International Conference on Computer and Information Technology, Shanghai, China, 2005, pp. 591-595.
  14. G. Bebis, T. Deaconu, and M. Georgiopoulos, "Fingerprint identification using Delaunay triangulation," in Proceedings of the International Conference on Information Intelligence and Systems, Bethesda, MD, 1999, pp. 452-459.
  15. M. A. Medina-Prrez, M. Garcia-Borroto, A. E. Gutierrez-Rodriguez, and L. Altamirano-Robles, "Improving fingerprint verification using minutiae triplets," Sensors, vol. 12, no. 3, pp. 3418-3437, 2012. https://doi.org/10.3390/s120303418
  16. X. Liang, T. Asano, and A. Bishnu, "Distorted fingerprint indexing using minutia detail and Delaunay triangle," in Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering, Banff, Canada, 2006, pp. 217-223.
  17. D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain, "FVC2000: fingerprint verification competition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 402-412, 2002. https://doi.org/10.1109/34.990140
  18. J. Bhatnagar and A. Kumar, "On estimating performance indices for biometric identification," Pattern Recognition, vol. 42, no. 9, pp. 1803-1815, 2009. https://doi.org/10.1016/j.patcog.2008.10.004
  19. D. Maio, D. Maltoni, R. Cappelli, J. Wayman, and A. Jain, "FVC2004: third fingerprint verification competition," in Biometric Authentication. Heidelberg: Springer, 2004, pp. 31-35.