Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fingerprint Features

  • Josef Bigun
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_50



Fingerprint features are parameters in epidermis images of a fingertip (the fingerprint) that can be utilized to extract information which is exclusively specific to a unique person. These parameters can be measured by computational techniques applied to a digital image obtained by a fingerprint sensing method, e.g., using live optical or solid-state scanners, and digitizing ink-rolled or latent fingerprint images. Such identity characterizing parameters include one or more specifics of ridge–valley direction and frequency, minutiae, and singular points. The fingerprint features should be reproducible and resilient to variation in the face of external factors such as aging, scars, wear, humidity, and method of collection.


Fingerprints consist of ridges alternating with valleys that mostly run in parallel but also change direction smoothly or may terminate abruptly. Other...

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  1. 1.
    Locard, A.: L’Identification des Récidivistes. A. Maloine, Paris (1909)Google Scholar
  2. 2.
    Bigun, J., Granlund, G.: Optimal orientation detection of linear symmetry. In: First International Conference on Computer Vision, ICCV, London, June 8–11, pp. 433–438. IEEE Computer Society, London (1987)Google Scholar
  3. 3.
    Kass, M., Witkin, A.: Analyzing oriented patterns. Comput. Vision Graph. Image Process. 37, 362–385 (1987)CrossRefGoogle Scholar
  4. 4.
    Bigun, J., Granlund, G., Wiklund, J.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE-PAMI 13(8), 775–790 (1991)Google Scholar
  5. 5.
    Granlund, G.: In search of a general picture processing operator. Comput. Graph. Image Process 8(2), 155–173 (1978)CrossRefGoogle Scholar
  6. 6.
    Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn. 28(11), 1657–1672 (1995). URL http://dx.doi.org/10.1016/0031-3203(95)00039-3
  7. 7.
    Grother, P., Tabassi, E.: Performance of biometric quality measures. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 531–543 (2007). URL http://dx.doi.org/10.1109/TPAMI.2007.1019
  8. 8.
    Fronthaler, H., Kollreider, K., Bigun, J., Fierrez, J., Alonso-Fernandez, F., Ortega-Garcia, J.: Fingerprint image quality estimation and its application to multi-algorithm verification. IEEE Trans. Inform. Forens. Security 3(2): 331–338 (2008)Google Scholar
  9. 9.
    Bigun, J.: Vision with Direction. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9(5), 846–859 (2000). URL http://dx.doi.org/10.1109/83.841531
  11. 11.
    Maio, D., Maltoni, D.: Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 27–40 (1997). URL http://www.computer.org/tpami/tp1997/i0027abs.htm
  12. 12.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Berlin (2003). URL http://bias.csr.unibo.it/maltoni/handbook/
  13. 13.
    Hong, L., Wand, Y., Jain, A.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE-PAMI 20(8), 777–789 (1998)Google Scholar
  14. 14.
    Chen, Y., Dass, S.C., Jain, A.K.: Fingerprint quality indices for predicting authentication performance. In: Audio- and Video-Based Biometric Person Authentication, p. 160 (2005). URL http://dx.doi.org/10.1007/11527923_17
  15. 15.
    Xiao, Q., Raafat, H.: Fingerprint image postprocessing: A combined statistical and structural approach. Pattern Recogn. 24 (10), 985–992 (1991). URL http://dx.doi.org/10.1016/0031-3203(91)90095-M
  16. 16.
    Hung, D.C.D.: Enhancement and feature purification of fingerprint images. Pattern Recogn. 26(11), 1661–1671 (1993). URL http://dx.doi.org/10.1016/0031-3203(93)90021-N
  17. 17.
    Shih, F.Y., Pu, C.C.: A skeletonization algorithm by maxima tracking on Euclidean distance transform. Pattern Recogn. 28(3), 331–341 (1995)CrossRefGoogle Scholar
  18. 18.
    Farina, A., Kovacs Vajna, Z.M., Leone, A.: Fingerprint minutiae extraction from skeletonized binary images. Pattern Recognition 32(5), 877–889 (1999). URL http://www.sciencedirect.com/science/article/B6V14-3WMK59F-D/2/bf21218ba618c9f63efb1663ea24a6f6
  19. 19.
    Fronthaler, H., Kollreider, K., Bigun, J.: Local feature extraction in fingerprints by complex filtering. In: S.Z.Li et al. (ed.) International Workshop on Biometric Recognition Systems – IWBRS 2005, Beijing, Oct. 22–23, LNCS 3781, pp. 77–84. Springer, Heidelberg (2005)Google Scholar
  20. 20.
    Maio, D., Maltoni, D.: Ridge-line density estimation in digital images. In: International Conference on Pattern Recognition, vol I, pp. 534–538 (1998). URL http://dx.doi.org/10.1109/ICPR.1998.711198
  21. 21.
    Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recogn 17, 295–303 (1984)CrossRefGoogle Scholar
  22. 22.
    Bazen, A., Gerez, S.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE-PAMI 24 (7), 905–919 (2002)Google Scholar
  23. 23.
    Bigun, J., Bigun, T., Nilsson, K.: Recognition by symmetry derivatives and the generalized structure tensor. IEEE-PAMI 26, 1590–1605 (2004)Google Scholar
  24. 24.
    Nilsson, K., Bigun, J.: Localization of corresponding points in fingerprints by complex filtering. Pattern Recogn. Lett. 24, 2135–2144 (2003)CrossRefGoogle Scholar
  25. 25.
    Wegstein, J.H.: An automated fingerprint identification system. Tech. Rep. Special Publication 500-89, National Bureau of Standards, NBS (1982). URL http://www.itl.nist.gov/iad/894.03/fing/Special_Publication_500-89.pdf
  26. 26.
    Novikov, S., Kot, V.: Singular feature detection and classification of fingerprints using Hough transform. In: E. Wenger, L. Dimitrov (eds.) Proc. of SPIE, vol. 3346, pp. 259–269 (1998)Google Scholar
  27. 27.
    Garcia, J.O., Aguilar, J.F., Simon, D., Gonzalez, J., Zanuy, M.F., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.J., Vivaracho, C., Escudero, D., Moro, Q.I.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vision Image Signal Process. 150 (6), 395–401 (2003). URL http://ieeexplore.ieee.org:80/xpls/abs_all.jsp?isNumber=2825%2&prod=JNL&arnumber=1263277&arSt=+395&ared=+401&arNumber=1263277

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Josef Bigun
    • 1
  1. 1.Embedded Intelligent Systems Center Halmstad University, IDEHalmstadSweden