Fingerprint Class Recognition for Securing EMV Transaction

B. Vibert, J. M. Le Bars, C. Rosenberger, C. Charrier

Abstract

Fingerprint analysis is a very important issue in biometry. The minutiae representation of a fingerprint is the most used modality to identify people or authorize access when using a biometric system. In this paper, we propose some features based on triangle parameters from the Delaunay triangulation of minutiae. We show the benefit of these features to recognize the type of a fingerprint without any access to the associated fingerprint image.

References

  1. Alok, A. K., Saha, S., and Ekbal, A. (2015). Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery. Soft Computing, pages 1-19.
  2. Aurenhammer, F. (1991). Voronoi diagramsa survey of a fundamental geometric data structure. ACM Computing Surveys (CSUR), 23(3):345-405.
  3. Chang, C.-C. and Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27.
  4. Charrier, C., Lézoray, O., and Lebrun, G. (2012). A machine learning regression scheme to design a frimage quality assessment algorithm. In Conference on Colour in Graphics, Imaging, and Vision, volume 2012, pages 35-42. Society for Imaging Science and Technology.
  5. Elguebaly, T. and Bouguila, N. (2015). A hierarchical nonparametric bayesian approach for medical images and gene expressions classification. Soft Computing, 19(1):189-204.
  6. EMVCo (2008). EMV integrated circuit card specifications for payment systems. Technical report, EMVCo.
  7. Fiérrez-Aguilar, J., Nanni, L., Ortega-Garcia, J., Cappelli, R., and Maltoni, D. (2005). Combining multiple matchers for fingerprint verification: a case study in fvc2004. In International Conference on Image Analysis and Processing, pages 1035-1042. Springer.
  8. Fiore, U., Palmieri, F., Castiglione, A., and De Santis, A. (2013). Network anomaly detection with the restricted boltzmann machine. Neurocomputing, 122:13-23.
  9. Gopi, M., Krishnan, S., and Silva, C. T. (2000). Surface reconstruction based on lower dimensional localized delaunay triangulation. In Computer Graphics Forum, volume 19, pages 467-478.
  10. Hsu, C.-W. and Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on, 13(3):415-425.
  11. Jain, A. K., Prabhakar, S., , and Hong, L. (1999a). A multichannel approach to fingerprint classification. InPattern Analysis and Machine Intelligence, IEEE Transactions on, volume 24, pages 248-359.
  12. Jain, A. K., Prabhakar, S., and Hong, L. (1999b). A multichannel approach to fingerprint classification. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(4):348-359.
  13. Jayaraman, U., Gupta, A. K., and Gupta, P. (2014). An efficient minutiae based geometric hashing for fingerprint database. Neurocomputing, 137:115-126.
  14. Kudo, M. and Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1):25-41.
  15. Kumar, M. et al. (2014). A novel fingerprint minutiae matching using lbp. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2014 3rd International Conference on, pages 1-4. IEEE.
  16. Kumar, S. U. and Inbarani, H. H. (2016). Neighborhood rough set based ecg signal classification for diagnosis of cardiac diseases. Soft Computing, pages 1-13.
  17. Labatut, P., Pons, J.-P., and Keriven, R. (2007). Efficient multi-view reconstruction of large-scale scenes using interest points, delaunay triangulation and graph cuts. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8. IEEE.
  18. Li, J., Du, Q., and Li, Y. (2015). An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Computing, pages 1-7.
  19. Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., and Jain, A. K. (2004). Fvc2004: Third fingerprint verification competition. In Biometric Authentication, pages 1-7. Springer.
  20. Oehlmann, L., Huckemann, S., and Gottschlich, C. (2015). Performance evaluation of fingerprint orientation field reconstruction methods. In Biometrics and Forensics (IWBF), 2015 International Workshop on, pages 1-6. IEEE.
  21. Palmieri, F., Fiore, U., and Castiglione, A. (2014). A distributed approach to network anomaly detection based on independent component analysis. Concurrency and Computation: Practice and Experience, 26(5):1113- 1129.
  22. Palmieri, F., Fiore, U., Castiglione, A., and De Santis, A. (2013). On the detection of card-sharing traffic through wavelet analysis and support vector machines. Applied Soft Computing, 13(1):615-627.
  23. Park, J., Bhuiyan, M. Z. A., Kang, M., Son, J., and Kang, K. (2016). Nearest neighbor search with locally weighted linear regression for heartbeat classification. Soft Computing, pages 1-12.
  24. Roy, B. R. and Trivedi, A. K. (2014). Construction of fingerprint orientation field from minutia points. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on, pages 1439-1442. IEEE.
  25. Shewchuk, J. R. (2002). Delaunay refinement algorithms for triangular mesh generation. Computational geometry, 22(1):21-74.
  26. Su, P. and Drysdale, R. L. S. (1995). A comparison of sequential delaunay triangulation algorithms. In Proceedings of the eleventh annual symposium on Computational geometry, pages 61-70. ACM.
  27. Vapnik, V. N. (1998). Statistical Learning Theory. Wiley, New York.
  28. Vibert, B., Christophe, C., Le Bars, J.-M., and Rosenberger, C. (2016). In what way is it possible to impersonate you bypassing fingerprint sensors ? In 15th International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.
  29. Vigila, S. A. M. C., Muneeswaran, K., and Antony, W. T. B. A. (2014). Biometric security system over finite field for mobile applications. IET Information Security, 9(2):119-126.
  30. Watson, C. I., Garris, M. D., Tabassi, E., Wilson, C. L., Mccabe, R. M., Janet, S., and Ko, K. (2007). Users guide to nist biometric image software (nbis). Technical report, NIST.
  31. Zhang, C., Lei, Y.-K., Zhang, S., Yang, J., and Hu, Y. (2016). Orthogonal discriminant neighborhood analysis for tumor classification. Soft Computing, 20(1):263-271.
  32. Zhang, Q. and Yan, H. (2004). Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognition, 37(11):2233- 2243.
Download


Paper Citation


in Harvard Style

Vibert B., M. Le Bars J., Rosenberger C. and Charrier C. (2017). Fingerprint Class Recognition for Securing EMV Transaction . In Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-209-7, pages 403-410. DOI: 10.5220/0006205704030410


in Bibtex Style

@conference{icissp17,
author={B. Vibert and J. M. Le Bars and C. Rosenberger and C. Charrier},
title={Fingerprint Class Recognition for Securing EMV Transaction},
booktitle={Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2017},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006205704030410},
isbn={978-989-758-209-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Fingerprint Class Recognition for Securing EMV Transaction
SN - 978-989-758-209-7
AU - Vibert B.
AU - M. Le Bars J.
AU - Rosenberger C.
AU - Charrier C.
PY - 2017
SP - 403
EP - 410
DO - 10.5220/0006205704030410