Skip to main content

Fingerprint Indexing

  • Reference work entry
  • 309 Accesses

Synonyms

Continuous classification; Fingerprint retrieval; fingerprint authentication; fingerprint identification

Definition

When matching a query fingerprint to a large fingerprint database for identification purposes, a critical issue is how to narrow down the search space. Indexing provides a mechanism to quickly determine if a query fingerprint is in the database and to retrieve those fingerprints that are most similar with the query, without searching the whole database.

Introduction

Fingerprint matching is one of the most popular and reliable biometric techniques used in automatic personal identification. Typically, fingerprint matching is based on low-level features determined by singularities in the finger ridge pattern known as minutiae. To be practical, matching should be robust to translation, rotation, scale, shear, occlusion, and clutter. In this context, matching two fingerprints implies finding a subset of minutiae in the first fingerprint that best match to a subset of...

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   449.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook on fingerprint recognition. Springer, Berlin (2003)

    Google Scholar 

  2. Lamdan, Y., Schwartz, J., Wolfson, H.: Affine invariant model-based object recognition. IEEE Trans. Robot. Automat. 6(5), 578–589 (1990)

    Article  Google Scholar 

  3. Califano, A., Mohan, R.: Multidimensional indexing for recognizing visual shapes. IEEE Trans. Pattern Analy. Mach. Intell. 16(4), 373–392 (1994)

    Article  Google Scholar 

  4. Bebis, G., Georgiopoulos, M., Shah, M., da Vitoria Lobo, N.: Indexing based on algebraic functions of views. Comput. Vision Image Understand. 72, 360–378 (1998)

    Article  Google Scholar 

  5. Lumini, A., Maio, D., Maltoni, D.: Continuous versus exclusive classification for fingerprint retrieval. Pattern Recogn. Lett. 18(10), 1027–1034 (1997)

    Article  Google Scholar 

  6. Cappelli, R., Lumini, A., Maio, D., Maltoni, D.: Fingerprint classification by directional image partitioning. IEEE Trans. Pattern Analy. Mach. Intell. 21(5), 402–421 (1999)

    Article  Google Scholar 

  7. Cappelli, R., Maio, D., Maltoni, D.: Multispace kl for pattern representation and classification. IEEE Trans. Pattern Analy. Mach. Intell. 23(9), 977–996 (2001)

    Article  Google Scholar 

  8. Li, J., Yau, W.Y., Wang, H.: Fingerprint indexing based on symmetrical measurement. Int. Conf. Pattern Recogn. 1, 1038–1041 (2006)

    Google Scholar 

  9. Germain, R., Califano, A., Colville, S.: Fingerprint matching using transformation parameter clustering. IEEE Computational Science and Engineering 4(4), 42–49 (1997)

    Article  Google Scholar 

  10. Bebis, G., Deaconu, T., Georgiopoulos, M.: Fingerprint identification using delaunay triangulation. In: IEEE, International Symposium on Information, Intelligence, and Systems, pp. 452–459 (1999)

    Google Scholar 

  11. Bhanu, B., Tan, X.: Fingerprint indexing based on novel features of minutiae triplets. IEEE Trans. Pattern Analy. Mach. Intell. 25(5), 616–622 (2003)

    Article  Google Scholar 

  12. Feng, J., Cai, A.: Fingerprint indexing using ridge invariants. Int. Conf. Pattern Recogn. 4, 433–436 (2006)

    Google Scholar 

  13. Ross, A., Mukherjee, R.: Augmenting ridge curves with minutiae triplets for fingerprint indexing. In: Prabhakar S., Ross A. (eds.) SPIE Defense and Security Symposium (Biometric Technology for Human Identification IV, vol. 6539) (2006)

    Google Scholar 

  14. Uz, T., Bebis, G., Erol, A., Prabhakar, S.: Minutiae-based template synthesis and matching using hierarchical delaunay triangulation. In: IEEE International Conference on Biometrics: Theory, Applications and Systems (2007)

    Google Scholar 

  15. Wolfson, H., Rigoutsos, I.: Geometric hashing: An overview. IEEE Comput. Sci. Eng. 4(4), 10–21 (1997)

    Article  Google Scholar 

  16. Bebis, G., Georgiopoulos, M., La Vitoria Lobo, N.: Using self-organizing maps to learn geometric hashing functions for model-based object recognition. IEEE Trans. Neural Networks 9(3), 560–570 (1998)

    Article  Google Scholar 

  17. Li, W., Bebis, G., Bourbakis, N.: Integrating algebraic functions of views with indexing and learning for 3D object recognition. IEEE Workshop on Learning in Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  18. Nene, S., Nayar, S.: Closest point search in high dimensions. In: Computer Vision and Pattern Recognition Conference, pp. 859–865 (1998)

    Google Scholar 

  19. Beis, J., Lowe, D.: Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In: Computer Vision and Pattern Recognition Conference. pp. 1000–1006 (1997)

    Google Scholar 

  20. Grimson, W., Huttenlocher, D., Jacobs, D.: A study of affine matching with bounded sensor error. Int. J. Comput. Vision 13(1), 7–32 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Bebis, G. (2009). Fingerprint Indexing. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_57

Download citation

Publish with us

Policies and ethics