Automatic Fingerprints Image Generation Using Evolutionary Algorithm

  • Ung-Keun Cho
  • Jin-Hyuk Hong
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Constructing a fingerprint database is important to evaluate the performance of an automatic fingerprint recognition system. Because of the difficulty in collecting fingerprint samples, there are only few benchmark databases available. Moreover, various types of fingerprints should be required to get a fair assessment on how robust the system is against various environments. This paper presents a novel method that generates various fingerprint images automatically from only a few training samples by using the genetic algorithm. Fingerprint images generated by the proposed method include similar characteristics of those collected from the corresponding real environment. Experiments with real fingerprints verify the usefulness of the proposed method.


fingerprint identification performance evaluation genetic algorithm image filtering image generation 


  1. 1.
    Pankanti, S., Prabhakar, S., Jain, A.: On the individuality of fingerprints. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1010–1025 (2002)CrossRefGoogle Scholar
  2. 2.
    Cappelli, R., Maio, D., Maltoni, D., Wayman, J.L., Jain, A.K.: Performance evaluation of fingerprint verification systems. IEEE Trans. Pattern Analysis and Machine Intelligence 28(1), 3–18 (2006)CrossRefGoogle Scholar
  3. 3.
    Khanna, R., Weicheng, S.: Automated fingerprint identification system (AFIS) benchmarking using the National Institute of Standards and Technology (NIST) Special Database 4. In: Proc. 28th Int. Carnahan Conf. on Security Technology, pp. 188–194 (1994)Google Scholar
  4. 4.
    Maltoni, D.: Generation of Synthetic Fingerprint Image Databases. In: Ratha, N., Bolle, R. (eds.) Automatic Fingerprint Recognition Systems, Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Simon-Zorita, D., Ortega-Garcia, J., Fierrez-Aguilar, J., Gonzalez-Rodriguez, J.: Image quality and position variability assessment in minutiae-based fingerprint verification. IEEE Proc. Vision, Image Signal Process 150(6), 402–408 (2003)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Prabhakar, S., Pankanti, S.: On the similarity of identical twin fingerprints. Pattern Recognition 35(11), 2653–2663 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Hong, J.-H., Yun, E.-K., Cho, S.-B.: A review of performance evaluation for biometrics systems. Int. J. Image and Graphics 5(2), 501–536 (2005)CrossRefGoogle Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)Google Scholar
  9. 9.
    Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. In: Proceedings of Computer Graphics SIGGRAPH, pp. 187–194 (1999)Google Scholar
  10. 10.
    Orlans, N., Piszcz, A., Chavez, R.: Parametrically controlled synthetic imagery experiment for face recognition testing. In: Proc. of the 2003 ACM SIGMM workshop on Biometrics methods and applications, pp. 58–64 (2003)Google Scholar
  11. 11.
    Cho, U.-K., Hong, J.-H., Cho, S.-B.: Evolutionary singularity filter bank optimization for fingerprint image enhancement. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 380–390. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, Reading, MA (1992)Google Scholar
  13. 13.
    Karu, K., Jain, A.: Fingerprint Classification. Pattern Recognition 29(3), 389–404 (1996)CrossRefGoogle Scholar
  14. 14.
    Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. IEEE Int. Conf. on Image Processing 1, 22–25 (2002)Google Scholar
  15. 15.
    Kang, H., Lee, B., Kim, H., Shin, D., Kim, J.: A study on performance evaluation of fingerprint sensors. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 574–583. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ung-Keun Cho
    • 1
  • Jin-Hyuk Hong
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Dept. of Computer Science, Yonsei University, Biometrics Engineering Research Center, 134 Sinchon-dong, Seodaemun-ku, Seoul 120-749Korea

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