Two Unconstrained Biometric Databases

  • Hélder P. Oliveira
  • Filipe Magalhães
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


In the last few years the research community has witnessed significant progress in biometric technology, due to the availability of a wide variety of databases. However, the available databases that are currently available present significant setbacks in terms of restricted access to data, low-resolution and restrictions imposed on individuals during the acquisition phase.

In this paper, two new public databases are described that have been created, with fingerprint and palm print images and their characteristics are compared with other databases available in the research community. The advantages of these databases are the great variety of individual characteristics, they have no restrictions during acquisition and they have manual ground truth annotation. They were presented in two different international competitions and have been used in research by different authors.


Database Biometrics Fingerprint Palmprint 


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  1. 1.
    Jain, A., Lin, H., Bolle, R.: On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4), 302–314 (1997)CrossRefGoogle Scholar
  2. 2.
    Maltoni, D., Cappelli, R.: Fingerprint recognition. In: Handbook of Biometrics, pp. 23–42. Springer US (2008)Google Scholar
  3. 3.
    East Shore Technologies,
  4. 4.
    Cappelli, R., Ferrara, M., Franco, A., Maltoni, D.: Fingerprint verification competition 2006. Biometric Technology Today 15(7-8), 7–9 (2007)CrossRefGoogle Scholar
  5. 5.
    Casia fingerprint database,
  6. 6.
    Polyu palmprint database,
  7. 7.
  8. 8.
    Magalhaes, F., Oliveira, H. P., Campilho, A.: SPD 2010 - Fingerprint Singular Points Detection Competition Database,
  9. 9.
    Magalhaes, F., Oliveira, H. P., Matos, H., Campilho, A.: HGC 2011 - Hand Geometric Points Detection Competition Database,
  10. 10.
    Srinivasan, V.S., Murthy, N.N.: Detection of singular points in fingerprint images. Pattern Recognition 25(2), 139–153 (1992)CrossRefGoogle Scholar
  11. 11.
    Pankanti, S., Prabhakar, S., Jain, A.K.: On the individuality of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1010–1025 (2002)CrossRefGoogle Scholar
  12. 12.
    Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recognition 29(3), 389–404 (1996)CrossRefGoogle Scholar
  13. 13.
    Jinwei, G., Jie, Z.: A novel model for orientation field of fingerprints. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 493–498 (2003)Google Scholar
  14. 14.
    Henry, E.R.: Classification and Uses of Fingerprints. George Routledge and Sons, London (1900)Google Scholar
  15. 15.
    Kong, A., Zhang, D., Kamel, M.: A survey of palmprint recognition. Pattern Recognition 42(7), 1408–1418 (2009)CrossRefGoogle Scholar
  16. 16.
    Wong, R., Shi, P.: Peg-free hand geometry recognition using hierarchical geometry and shape matching. In: IAPR Workshop on Machine Vision Applications, pp. 281–284 (2002)Google Scholar
  17. 17.
    Montalvao, J., Molina, L., Canuto, J.: Robust hand image processing for biometric application. Pattern Analysis and Applications 13, 397–407 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Magalhães, F., Oliveira, H.P., Campilho, A.C.: A New Method for the Detection of Singular Points in Fingerprint Images. In: Proceedings IEEE Workshop on Applications of Computer Vision, pp. 157–162 (2009)Google Scholar
  19. 19.
    Sierra, A., Avila, C., del Pozo, G., Casanova, J.: Unconstrained and Contactless Hand Geometry Biometrics. Sensors 11(11), 10143–10164 (2011)CrossRefGoogle Scholar
  20. 20.
    de Santos Sierra, A., Sánchez Ávila, C., Guerra Casanova, J., del Pozo, G.B.: Invariant Hand Biometrics Feature Extraction. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 108–115. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Mestetskiy, L., Bakina, I., Kurakin, A.: Hand Geometry Analysis by Continuous Skeletons. In: Kamel, M., Campilho, A. (eds.) ICIAR 2011, Part II. LNCS, vol. 6754, pp. 130–139. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Matos, H., Oliveira, H.P., Magalhães, F.: Hand-Geometry Based Recognition System - A Non Restricted Acquisition Approach. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part II. LNCS, vol. 7325, pp. 38–45. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hélder P. Oliveira
    • 1
  • Filipe Magalhães
    • 1
  1. 1.INESC TEC (formerly INESC Porto) and Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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