Fuzzy Extractors for Minutiae-Based Fingerprint Authentication

  • Arathi Arakala
  • Jason Jeffers
  • K. J. Horadam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


We propose an authentication scheme using fingerprint biometrics, protected by a construct called a Fuzzy Extractor. We look at a new way of quantizing and digitally representing the minutiae measurements so that a construct called PinSketch can be applied to the minutiae. This is converted to a Fuzzy Extractor by tying some random information to the minutiae measurements. We run a matching algorithm at chosen quantization parameters and show that the authentication accuracy is within acceptable limits. We demonstrate that our authentication system succeeds in protecting the users’ identity.


Authentication System Biometric Template Local Reference Frame Minutia Position Codeword Length 
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.


  1. 1.
    Ross, A., Shah, J., Jain, A.K.: Towards reconstructing fingerprints from minutiae points. In: Proc. SPIE, Biometric Technology for Human Identification II, vol. 5779, pp. 68–80 (March 2005)Google Scholar
  2. 2.
    Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: Tsudik, G. (ed.) Sixth ACM Conference on Computer and Communications Security, pp. 28–36. ACM Press, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Juels, A., Sudan, M.: A Fuzzy vault scheme. In: Proc. of IEEE ISIT, Lausanne, Switzerland, June 30-July 5, 2002, p. 408. IEEE Press, Los Alamitos (2002)CrossRefGoogle Scholar
  4. 4.
    Linnartz, J.-P., Tuyls, P.: New shielding functions to enhance privacy and prevent misuse of biometric templates. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 393–402. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Tuyls, P., Goseling, J.: Capacity and Examples of Template-Protecting Biometric Authentication Systems. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 158–170. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data, Cryptology ePrint Archive, Report, 2003/235 (2003), Available
  7. 7.
    Uludag, U., Jain, A.K.: Securing fingerprint template: fuzzy vault with helper data. In: Proc. IEEE Workshop on Privacy Research In Vision, NY, June 22, 2006, p. 163 (2006)Google Scholar
  8. 8.
    Tuyls, P., Akkermans, A.H.M., Kevenaar, T.A.M., Schrijen, G.-J., Bazen, A.M., Veldhuis, R.N.J.: Practical Biometric Authentication with Template Protection. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 436–446. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Jeffers, J., Arakala, A.: Minutiae-based Structures for a Fuzzy Vault. In: Proc. of 2006 Biometrics Symposium, MD, USA, September 19-21, 2006 (2006)Google Scholar
  10. 10.
    Shoup, V.: A Computational Introduction to Number Theory and Algebra, p. 125. Cambridge University Press, Cambridge (2005)zbMATHGoogle Scholar
  11. 11.
    Harmon, K., Reyzin, L.: Implementation of algorithms from the paper Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data (accessed October 3, 2006) (Online), Available:
  12. 12.
    Shoup, V.: NTL: A Library for doing Number Theory (accessed October 9, 2006) (Online), available:

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arathi Arakala
    • 1
  • Jason Jeffers
    • 1
  • K. J. Horadam
    • 1
  1. 1.School of Mathematical and Geospatial Sciences, RMIT University, 368-374 Swanston Street, Melbourne 3000 

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