Journal of Real-Time Image Processing

, Volume 9, Issue 1, pp 95–109 | Cite as

A hardware solution for real-time intelligent fingerprint acquisition

  • Rosario ArjonaEmail author
  • Iluminada Baturone
Special Issue


The first step in any fingerprint recognition system is the fingerprint acquisition. A well-acquired fingerprint image results in high-resolution accuracy and low computational effort of processing. Hence, it is very useful for the recognition system to evaluate recognition confidence level to request new fingerprint samples if the confidence level is low, and to facilitate recognition process if the confidence level is high. This paper presents a hardware solution to ensure a successful and friendly acquisition of the fingerprint image, which can be incorporated at low cost into an embedded fingerprint recognition system due to its small size and high speed. The solution implements a novel technique based on directional image processing that allows not only the estimation of fingerprint image quality, but also the extraction of useful information (in particular, singular points). The digital architecture of the module is detailed and their features in terms of resource consumption and processing speed are illustrated with implementation results into FPGAs from Xilinx. Performance of the solution has been verified with fingerprints from several standard databases that have been acquired with sensors of different sizes and technologies (optical, capacitive, and thermal sweeping).


Fingerprint acquisition Fingerprint quality Biometric hardware FPGA hardware design CAD tools 



This work was partially funded by Junta de Andalucía under the Project P08-TIC-03674 (with support from the PO FEDER-FSE de Andalucía 2007–2013), by Spanish Ministerio de Economía y Competitividad under the Project TEC2011-24319 (with support from FEDER), and by the European Community through the MOBY-DIC Project FP7-INFSO-ICT-248858 (


  1. 1.
    Alonso-Fernández, F., Fierrez, J., Ortega-García, J., González-Rodríguez, J., Fronthaler, H., Kollreider, K., Bigun, J.: A comparative study of fingerprint image-quality estimation methods. IEEE Trans. Inf. Forensics Secur. 2, 734–743 (2007)CrossRefGoogle Scholar
  2. 2.
    Cantó, E., Fons, M., López, M., Ramos, R.: Acceleration of complex algorithms on a fast reconfigurable embedded system on Spartan-3. In: International Conference on Field Programmable Logic and Applications (2009)Google Scholar
  3. 3.
    Cappelli, R., Lumini, A., Maio, D., Maltoni, D.: Fingerprint classification by directional image partitioning. IEEE Trans. Pattern Anal. Mach. Intel. 21, 402–421 (1999)CrossRefGoogle Scholar
  4. 4.
    Chao, G., Lee S., Hornq, S.: Embedded fingerprint verification system. In: Proceedings of the 11th International Conference on Parallel and Distributed Systems (2005)Google Scholar
  5. 5.
    Chen, Y., Dass, S., Jain, A.: Fingeprint quality indices for predicting authentication performance. In: Proceedings of the 5th International Conference on Audio- and Video-based Biometric Person Authentication. Springer, Berlin (2005)Google Scholar
  6. 6.
    Chen, D., Ji, X., Fan, F., Zhang, J., Guo, L., Meng, W.: Comparative analysis of fingerprint orientation field algorithms. In: Proceedings of the 5th International Conference on Image and Graphics (2009)Google Scholar
  7. 7.
    Cheng, J.G., Tian, J.: Fingerprint enhancement with dyadic scale-space. Pattern Recognit. Lett. 25, 1273–1284 (2004)CrossRefGoogle Scholar
  8. 8.
    Fons, M., Fons, F., Canyellas, N., Cantó, E., López, M.: Hardware-software co-design of an automatic fingerprint acquisition system. In: Proceedings of the IEEE International Symposium on Industrial Electronics (2005)Google Scholar
  9. 9.
    Fons, F., Fons, M., Cantó, E., López, M.: Flexible hardware for fingerprint image processing. In: Research in Microelectronics and Electronics Conference, PRIME (2007)Google Scholar
  10. 10.
    Fons, F., Fons, M., Cantó, E., López, M.: Real-time embedded systems powered by FPGA dynamic partial self-reconfiguration: A case study oriented to biometric recognition applications. J. Real-Time Image Process. Spec. Issue 1–23 (2011)Google Scholar
  11. 11.
    FVC 2002 database. (2002)
  12. 12.
    FVC 2006 database. (2006)
  13. 13.
    García, M.L., Navarro, E.F.C.: FPGA implementation of a ridge extraction fingerprint algorithm based on microblaze and hardware coprocessor. In: International Conference on Field Programmable Logic and Applications (2006)Google Scholar
  14. 14.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intel. 20, 777–789 (1998)CrossRefGoogle Scholar
  15. 15.
    Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recognit. 17, 295–303 (1984)CrossRefGoogle Scholar
  16. 16.
    Kim, S., Lee, K., Han, S., Yoon, E.: A CMOS fingerprint system-on-a-chip with adaptable pixel networks and column-parallel processors for image enhancement and recognition. IEEE J. Solid State Circuits 43, 2558–2567 (2008)CrossRefGoogle Scholar
  17. 17.
    Kovesi, P.: Directional image algorithm.∼pk/research/matlabfns/ (2005)
  18. 18.
    Lam, H.K., Hou, Z., Yau, W.Y., Chen, T.P., Li, J.: A Systematic topological method for fingerprint singular point detection. In: 10th International Conference on Control, Automation, Robotics and Vision (2008)Google Scholar
  19. 19.
    Maltoni, D., Maio, D., Jain, A. K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, Berlin (2009)Google Scholar
  20. 20.
    Militello, C., Conti, V., Sorbello, F., Vitabile, S.: A novel embedded fingerprint authentication system based on singularity points. In: International Conference on Complex, Intelligent and Software Intensive Systems (2008)Google Scholar
  21. 21.
    Mohammadi, S., Farajzadeh, A.: Fingerprint reference point detection using orientation field and curvature measurements. In: IEEE International Conference on Intelligent Computing and Intelligent Systems (2009)Google Scholar
  22. 22.
    Neurotechnology: Fingerprint commercial scanners. (2012)
  23. 23.
    Nilsson, K., Bigun, J.: Localization of corresponding points in fingerprints by complex filtering. Pattern Recognit. Lett. 24, 2135–2144 (2003)CrossRefGoogle Scholar
  24. 24.
    Ohtsuka, T., Takahashi, T.: A new detection approach for the fingerprint core location using extended relation graph. IEICE Trans. Inf. Syst. 88, 2308–2312 (2005)CrossRefGoogle Scholar
  25. 25.
    Park, C.H., Lee, J.J., Smith, M.J.T., Park, K.H.: Singular point detection by shape analysis of directional fields in fingerprints. Pattern Recognit. 39, 839–855 (2006)CrossRefzbMATHGoogle Scholar
  26. 26.
    Prabhakar, S.: Fingerprint classification and matching using a filterbank. Thesis (2000)Google Scholar
  27. 27.
    Rosa, L.: Filterbank-based fingerprint matching. (2007)
  28. 28.
    Srinivasan, V.S., Murthy, N.N.: Detection of singular points in fingerprint images. Pattern Recognit. 25, 139–153 (1992)CrossRefGoogle Scholar
  29. 29.
    Tabassi, E., Wilson, C. L., Watson, C. I.: Fingerprint image quality. NIST Internal Report 7151, National Institute for Standards and Technology (2004)Google Scholar
  30. 30.
    Xie, S.J., Yoon, S., Shin, J., Park, D.S.: Effective fingerprint quality estimation for diverse capture sensors. Sensors 10, 7896–7912 (2010)CrossRefGoogle Scholar
  31. 31.
    Xie, S.J., Yang, J., Gong, H., Yoon, S., Park, D.S.: Intelligent fingerprint quality analysis using online sequential extreme learning machine. Soft Computing—A Fusion of Foundations, Methodologies and Applications, Springer (2012)Google Scholar
  32. 32.
    Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dept. Electrónica y Electromagnetismo (University of Seville)Microelectronics Institute of Seville (IMSE-CNM-CSIC)SevilleSpain

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