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Automatic Fingerprints Image Generation Using Evolutionary Algorithm

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New Trends in Applied Artificial Intelligence (IEA/AIE 2007)

Abstract

Constructing a fingerprint database is important to evaluate the performance of automatic fingerprint recognition systems. Because of the difficulty in collecting fingerprint samples, there are only few benchmark databases available. Moreover, various types of fingerprints are required to measure how robust the system is in 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 a corresponding real environment. Experiments with real fingerprints verify the usefulness of the proposed method.

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References

  1. Pankanti, S., Prabhakar, S., Jain, A.: On the individuality of fingerprints. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1010–1025 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Maltoni, D.: Generation of Synthetic Fingerprint Image Databases. In: Ratha, N., Bolle, R. (eds.) Automatic Fingerprint Recognition Systems, Springer, Heidelberg (2004)

    Google Scholar 

  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)

    Article  Google Scholar 

  6. Jain, A., Prabhakar, S., Pankanti, S.: On the similarity of identical twin fingerprints. Pattern Recognition 35(11), 2653–2663 (2002)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  8. Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley, London (1989)

    Google Scholar 

  9. Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. In: Proc. Computer Graphics SIGGRAPH, pp. 187–194 (1999)

    Google Scholar 

  10. Orlans, N., Piszcz, A., Chavez, R.: Parametrically controlled synthetic imagery experiment for face recognition testing. In: Proc. ACM SIGMM workshop on Biometrics Methods and Applications, pp. 58–64. ACM Press, New York (2003)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  12. Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, London (1992)

    Google Scholar 

  13. Karu, K., Jain, A.: Fingerprint Classification. Pattern Recognition 29(3), 389–404 (1996)

    Article  Google Scholar 

  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. Kang, H., Lee, B., Kim, H., Shin, D., Kim, J.: A study on performance evaluation of fingerprint sensors. In: Proc. 4th Int. Conf. Audio-and Video-based Biometric Person Authentication, pp. 574–583 (2003)

    Google Scholar 

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Hiroshi G. Okuno Moonis Ali

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Cho, UK., Hong, JH., Cho, SB. (2007). Automatic Fingerprints Image Generation Using Evolutionary Algorithm. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_44

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  • DOI: https://doi.org/10.1007/978-3-540-73325-6_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73322-5

  • Online ISBN: 978-3-540-73325-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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