An Enhancement of the Usage of the Poincare Index for the Detection and Classification of Characteristic Points in Dactylograms

  • Angélica González
  • Marco A. Ameller F.
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)


In order to identify subjects in a convenient and efficient way one must use some special feature that makes it possible to discriminate between persons. Some of the features are biometric in nature, such as iris texture, hand shape, the human face, and of course finger prints. These play an important role in many automatic identification systems, since every person is believed to have a unique set of fingerprints. Before a fingerprint image can be looked up in a database, it has to be classified into one of 5 types in order to reduce processing times.


Singular Points Poincare Index Ridge field direction detection 


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  1. 1.
    Karu, K., Jain, A.K.: Fingerprint Classification, A Technical Report from Michigan State University (1995)Google Scholar
  2. 2.
    Henry, E.R.: Classification and uses of fingerprints. George Routledge and Sons, London (1900)Google Scholar
  3. 3.
    Miller, B.: Vital signs of identity. IEEE Spectrum 31(2), 22–30 (1994)CrossRefGoogle Scholar
  4. 4.
    Srinivasan, V.S., Murthy, N.N.: Detection of Singular Points in Fingerprint Images. Pattern Recognition 25(2), 139–153 (1992)CrossRefGoogle Scholar
  5. 5.
    Watson, C.I., Wilson, C.L.: NIST Special Database 4. Fingerprint Database. National Institute of Standard and Technology (March 1992)Google Scholar
  6. 6.
    Watson, C.I.: NIST Special Database 9: Mated Fingerprint Card Pairs. National Institute of standard and Technology (February 1993)Google Scholar
  7. 7.
    Rao, A.R.: A Taxonomy for Texture Description and Identification. Springer, New York (1990)zbMATHGoogle Scholar
  8. 8.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. Pattern Anal. Machine Intell. 20, 777–789 (1998)CrossRefGoogle Scholar
  9. 9.
    Woods, K., Kegelmeyer, W.P., Bowyer, K.W.: Combination of Multiple Classifiers Using Local Accuracy Estimates. IEEE Trans. Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Angélica González
    • 1
  • Marco A. Ameller F.
    • 1
    • 2
  1. 1.Computers and Automation DepartmentUniversity of SalamancaSalamancaSpain
  2. 2.Computers Engineering DepartmentAutonomous University Tomas FriasPotosíSpain

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