Knuckle Recognition for Human Identification

  • Michał Choraś
  • Rafał Kozik
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


This paper is the continuation of our previous work - hereby we report new results proving the effectiveness of our knuckle recognition method based on texture features. We use Probabilistic Hough Transform and SURF features as well as the 3-step classification methodology. Hereby we present promising results achieved for recently published PolyU knuckle database.


Query Image Feature Extraction Method Equal Error Rate Biometric System 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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  1. 1.
    Fratric, I., Ribaric, S.: Real-Time Model-Based Hand Localization for Unsupervised Palmar Image Acquisition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1280–1289. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Methani, C., Namboodiri, A.M.: Pose Invariant Palmprint Recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 577–586. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Zhang, L., Zhang, L., Zhang, D.: Finger-knuckle-print: a new biometric identifier. In: Proceedings of the IEEE International Conference on Image Processing (2009)Google Scholar
  5. 5.
    Kozik, R., Choras, M.: Combined Shape and Texture Information for Palmprint Biometrics. Journal of Information Assurance and Security 5(1), 58–63 (2010)Google Scholar
  6. 6.
  7. 7.
    Kumar, A., Zhou, Y.: Human Identification using Knuckle Codes. In: Proc. BTAS (2009)Google Scholar
  8. 8.
    Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Information Forensics and Security 4(1), 98–110 (2009)CrossRefGoogle Scholar
  9. 9.
    Kumar, A., Zhou, Y.: Personal identification using finger knuckle orientation features. Electronics Letters 45(20) (September 2009)Google Scholar
  10. 10.
    Zhang, L., Zhang, L., Zhang, D., Hailong Zhu, H.: Online Finger-Knuckle-Print Verification for Personal Authentication. Pattern Recognition 43(7), 2560–2571 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    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
  12. 12.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. 511–518 (2001)Google Scholar
  13. 13.
    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
  14. 14.
    Choraś, K.R.: Knuckle Biometrics Based on Texture Features. In: Proc. of International Workshop on Emerging Techniques and Challenges for Hand-based Biometrics (ETCHB 2010), Stambul, August 2010.IEEE CS Press, Los Alamitos (2010)Google Scholar
  15. 15.
    Choraś, K.R.: Knuckle Biometrics for Human Identification. In: Choras (ed.) Image Processing and Communication Challenges 2. ASC, pp. 95–102. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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