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Improving Fingerprint Recognition Based on Crease Detection

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Book cover Biometric Authentication (ICBA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3072))

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Abstract

Conventional algorithms for fingerprint recognition are mainly based on minutiae information. But it is difficult to extract minutiae accurately and robustly for elder people. One of main reasons is that there are too many creases on the fingertips of elder people. In this paper, we will propose a novel algorithm to improve fingerprint recognition based on crease detection. First, creases are extracted by using some special filters. Then the minutiae detected by using conventional algorithms can be further processed and those on or near the creases are discarded as false minutiae. The experimental results show that the performance can be improved by applying crease detection to discard the false minutiae. The false rejection rate can be reduced 6% on average for the fingerprints with creases.

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References

  1. Jain, A., Bolle, R., Pankanti, S. (eds.): BIOMETRICS: Personal Identification in Networked Society. Kluwer, New York (1999)

    Google Scholar 

  2. Zhang, D.: Automated biometrics: Technologies and systems. Kluwer Academic Publisher, USA (2000)

    Google Scholar 

  3. Jain, A., Hong, L.: On-line fingerprint verification. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(4), 302–314 (1997)

    Article  Google Scholar 

  4. Jain, A., Hong, L., Pankanti, S., Bolle, R.: An Identity Authentication System Using Fingerprints. Proceedings of the IEEE 85(9), 1365–1388 (1997)

    Article  Google Scholar 

  5. Wu, C., Zhou, J., Bian, Z., Rong, G.: Robust Crease Detection in Fingerprint Images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 505–510 (2003)

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  6. Acton, S.T., Mukherjee, D.P., Havlicek, J.P., Bovik, A.C.: Fingerprint Classification Using an AM-FM Model. IEEE Transactions on Image Processing 10(6), 951–954 (2001)

    Article  Google Scholar 

  7. Zhou, J., Gu, J.: A Model-based Method for the Computation of Fingerprints’ Orientation Field. IEEE Trans. On Image Processing 12(3) (2004)

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhou, J., Wu, C., Bian, Z., Zhang, D. (2004). Improving Fingerprint Recognition Based on Crease Detection. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_40

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  • DOI: https://doi.org/10.1007/978-3-540-25948-0_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22146-3

  • Online ISBN: 978-3-540-25948-0

  • eBook Packages: Springer Book Archive

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