Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming

  • Wannasak Wetcharaporn
  • Nachol Chaiyaratana
  • Sanpachai Huvanandana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper presents the use of a genetic algorithm and genetic programming for the enhancement of an automatic fingerprint identification system (AFIS). The recognition engine within the original system functions by transforming the input fingerprint into a feature vector or fingercode using a Gabor filter bank and attempting to create the best match between the input fingercode and the database fingercodes. A decision to either accept or reject the input fingerprint is then carried out based upon whether the norm of the difference between the input fingercode and the best-matching database fingercode is within the threshold or not. The efficacy of the system is in general determined from the combined true acceptance and true rejection rates. In this investigation, a genetic algorithm is applied during the pruning of the fingercode while the search by genetic programming is executed for the purpose of creating a mathematical function that can be used as an alternative to the norm operator. The results indicate that with the use of both genetic algorithm and genetic programming the system performance has improved significantly.


Genetic Algorithm Feature Vector Redundant Feature False Acceptance Rate False Rejection Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Galton, F.: Finger Prints. Macmillan, London (1892)Google Scholar
  2. 2.
    Henry, E.R.: Classification and Uses of Finger Prints. HM Stationary Office, London (1905)Google Scholar
  3. 3.
    Moayer, B., Fu, K.S.: A tree system approach for fingerprint pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(3), 376–387 (1986)CrossRefGoogle Scholar
  4. 4.
    Blue, J.L., Candela, G.T., Grother, P.J., Chellappa, R., Wilson, C.L.: Evaluation of pattern classifiers for fingerprint and OCR applications. Pattern Recognition 27(4), 485–501 (1994)CrossRefGoogle Scholar
  5. 5.
    Rao, T.C.M.: Feature extraction for fingerprint classification. Pattern Recognition 8(3), 181–192 (1976)MATHCrossRefGoogle Scholar
  6. 6.
    Hrechak, A.K., McHugh, J.A.: Automated fingerprint recognition using structural matching. Pattern Recognition 23(8), 893–904 (1990)CrossRefGoogle Scholar
  7. 7.
    Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recognition 29(3), 389–404 (1996)CrossRefGoogle Scholar
  8. 8.
    Hong, L., Jain, A.K.: Classification of fingerprint images. In: Proceedings of the 11th Scandinavian Conference on Image Analysis, Kangerlussuaq, Greenland (1999)Google Scholar
  9. 9.
    Cho, B.-H., Kim, J.-S., Bae, J.-H., Bae, I.-G., Yoo, K.-Y.: Core-based fingerprint image classification. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 859–862 (2000)Google Scholar
  10. 10.
    Mitra, S., Pal, S.K., Kundu, M.K.: Fingerprint classification using a fuzzy multilayer perceptron. Neural Computing and Applications 2(4), 227–233 (1994)CrossRefGoogle Scholar
  11. 11.
    Halici, U., Ongun, G.: Fingerprint classification through self-organizing feature maps modified to treat uncertainties. Proceedings of the IEEE 84(10), 1497–1512 (1996)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(4), 348–359 (1999)CrossRefGoogle Scholar
  13. 13.
    Coetzee, L., Botha, E.C.: Fingerprint recognition in low quality images. Pattern Recognition 26(10), 1441–1460 (1993)CrossRefGoogle Scholar
  14. 14.
    Farina, A., Kovács-Vajna, Z.M., Leone, A.: Fingerprint minutiae extraction from skeletonized binary images. Pattern Recognition 32(5), 877–889 (1999)CrossRefGoogle Scholar
  15. 15.
    Fan, K.C., Liu, C.W., Wang, Y.K.: A randomized approach with geometric constraints to fingerprint verification. Pattern Recognition 33(11), 1793–1803 (2000)CrossRefGoogle Scholar
  16. 16.
    Tan, X., Bhanu, B.: Robust fingerprint identification. In: Proceedings of the, International Conference on Image Processing, Rochester, NY, pp. I-277–I-280 (2002)Google Scholar
  17. 17.
    Tan, X., Bhanu, B.: Fingerprint matching by genetic algorithms. In: Late Breaking Papers at the 2002 Genetic and Evolutionary Computation Conference, New York, NY, pp. 435–442 (2002)Google Scholar
  18. 18.
    Tan, X., Bhanu, B.: Fingerprint verification using genetic algorithms. In: Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, Orlando, FL, pp. 79–83 (2002)Google Scholar
  19. 19.
    Tan, X., Bhanu, B.: Fingerprint matching by genetic algorithms. Pattern Recognition 39(3), 465–477 (2006)MATHCrossRefGoogle Scholar
  20. 20.
    Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Transactions on Image Processing 9(5), 846–859 (2000)CrossRefGoogle Scholar
  21. 21.
    Goldberg, D.E.: Genetic Algorithms: In Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  22. 22.
    Koza, J.R.: Genetic Programming: On the Programming by Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  23. 23.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wannasak Wetcharaporn
    • 1
  • Nachol Chaiyaratana
    • 1
  • Sanpachai Huvanandana
    • 2
  1. 1.Research and Development Center for Intelligent SystemsKing Mongkut’s Institute of Technology North BangkokBangkokThailand
  2. 2.Department of Electrical EngineeringChulachomklao Royal Military AcademyNakhonnayokThailand

Personalised recommendations