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Knuckle Biometrics for Human Identification

  • Michał Choraś
  • Rafał Kozik
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 84)

Summary

In this paper we present human identification method based on knuckle biometrics also termed as FKP (finger-knuckle-print). Knuckle is a part of hand, and therefore, is easily accessible, invariant to emotions and other behavioral aspects (e.g. tiredness) and most importantly is rich in texture features which usually are very distinctive. The major contribution of this paper are texture-based knuckle features and their evaluation using IIT Delhi knuckle image database.

Keywords

Feature Vector Query Image Feature Extraction Method Equal Error Rate Template Image 
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.

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References

  1. 1.
    Morales, A., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: A knuckles texture verification method in a transformed domain. In: Proc. of 1st Spanish Workshop on Biometrics (on CD), Girona, Spain (2007)Google Scholar
  2. 2.
    Kumar, A., Zhou, Y.: Human Identification using Knuckle Codes. In: Proc. BTAS (2009)Google Scholar
  3. 3.
    Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Information Forensics and Security 4(1), 98–110 (2009)CrossRefGoogle Scholar
  4. 4.
    Kumar, A., Zhou, Y.: Personal identification using finger knuckle orientation features. Electronics Letters 45(20) (2009)Google Scholar
  5. 5.
    Zhang, L., Zhang, L., Zhang, D.: Finger-knuckle-print verification based on band-limited phase-only correlation. In: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, pp. 141–148 (2009)Google Scholar
  6. 6.
  7. 7.
    Zhang, L., Zhang, L., Zhang, D., Hailong, Z.H.: Online Finger-Knuckle-Print Verification for Personal Authentication. Pattern Recognition 43(7), 2560–2571 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on CVPR 2001, vol. 1, pp. 511–518 (2001)Google Scholar
  9. 9.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Choraś, M., Kozik, R.: Feature Extraction Method for Contactless Palmprint Biometrics. In: Huang, D.-S., et al. (eds.) ICIC 2010. CCIS, vol. 93, pp. 435–442. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michał Choraś
    • 1
  • Rafał Kozik
    • 1
  1. 1.Image Processing GroupInstitute of Telecommunications, UT&LS Bydgoszcz 

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