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Fingerprint Hardening with Randomly Selected Chaff Minutiae

  • Alper Kanak
  • İbrahim Sog̃ukpınar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Since fingerprints provide a reliable alternative for traditional password based security systems, they gain industry and citizen acceptance. However, due to the higher uncertainty and inherent complexity associated with biometrics, using pure biometric traits does not present a reliable security system especially for large populations. This paper addresses this problem by proposing a hardening scheme which combines the fingerprint minutiae-based template and user-specific pseudo random data to enhance security. In the proposed scheme, a set of randomly selected user-specific chaff minutiae features are stored in a smartcard and a subset of this set is used at each acquisition. The set of chaff minutiae is combined with the template set and scrambled to form a fixed-length hardened feature. The graph based dynamic matching algorithm is transparent to the proposed hardening scheme anyhow it runs as if pure original template and query features are used. Our experiments show that biometric hardening reduces error rate to 0% with several orders of magnitude separation between genuine and impostor populations.

Keywords

Authentication System False Reject Rate Biometric Template Fuzzy Vault False Accept Rate 
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.
    Ratha, N.K., Connel, J.H., Bolle, R.: Enhancing Security and Privacy in Biometrcis-based Authentication System. IBM Systems Jour. 3(40), 614–634 (2001)CrossRefGoogle Scholar
  2. 2.
    Teoh, A.B.J., Ngo, D.C.L., Goh, A.: Personalised Cryptographic Key Generation Based on FaceHashing. Computers & Security 23(7), 606–614 (2004)CrossRefGoogle Scholar
  3. 3.
    Soutar, C., Roberge, D., Stojanov, S.A., Gilroy, R., Vijaya Kumar, B.V.K.: Biometric Encryption Using Image Processing. In: Proc. SPIE, Optical Security and Counterfeit Deterrence Techniques II. vol. 3314, pp. 178–188 (1998)Google Scholar
  4. 4.
    Savvides, M.,Vijaya Kumar, B.V.K., and Khosla, P.K.: Cancelable Biometric Filters for Face Recognition. In: Proc. of Int. Conf. on Pattern Recognition, pp. 922–925 (2004)Google Scholar
  5. 5.
    Ratha, N.K., Connel, J.H., Bolle, R., Chikkerur, S.: Cancelable Biometrics: A Case Study in Fingerprints. In: Proc. of Int. Conf. on Pattern Recognition (2006)Google Scholar
  6. 6.
    Davida, G.I., Frankel, Y., Matt, B.: On Enabling Secure Applications Through OFF-line Biometric Identification. In: Proc. IEEE Symp. Privacy and Security, pp. 148–157 (1998)Google Scholar
  7. 7.
    Monrose, F., Reiter, M.K., Wetsel, S.: Password Hardening Based on Keystroke Dynamics. In: Proc. ACM Conf. Computer & Communication Security, pp. 73–82 (1999)Google Scholar
  8. 8.
    Monrose, F., Reiter, M.K., Li, Q., Wetsel, S.: Using Voice to Generate Cryptographic Keys. In: Proc. 2001: A Speaker Odyssey, Speaker Recognition Workshop, pp. 237–242 (2001)Google Scholar
  9. 9.
    Juels, A., Sudan, M.: A Fuzzy Vault Scheme. Des. Codes Cryptography 38(2), 237–257 (2006)CrossRefMathSciNetzbMATHGoogle Scholar
  10. 10.
    Juels, A., Wattenberg, M.: A Fuzzy Commitment Scheme. In: Tsudik, G. (ed.) Proc. ACM Conf. Computer & Communications Security, pp. 28–36 (1999)Google Scholar
  11. 11.
    Uludag, U., Jain, A.K.: Securing Fingerprint Template: Fuzzy Vault with Helper Data. In: Proc. IEEE Workshop on Privacy Research in Vision, New York City, NY (2006)Google Scholar
  12. 12.
    Clancy, T.C., Kiyavash, N., Lin, D.J.: Secure Smartcard-based Fingerprint Authentication. In: Proc. ACM SIGMM Multimedia, Biometrics Methods & Applications Workshop, pp. 45–52 (2003)Google Scholar
  13. 13.
    Uludag, U., Pankant, S., Prabhakar, S., Jain, A.K.: Biometric Cryptosystems: Issues and Challenges. Proc. of the IEEE 92(6) (2004)Google Scholar
  14. 14.
    Tulyakov, S., Farooq, F., Govindaraju, V.: Symmetric Hash Functions for Fingerprint Minutiae. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 30–38. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Chikkerur, S., Cartwright, A.N., Govindaraju, V.: K-plet and CBFS: A Graph based Fingerprint Representation and Matching Algorithm. In: Int. Conf. Biometrics (2006)Google Scholar
  16. 16.
    Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint Enhancement Using STFT Analysis. Jour. Pattern Recogn. 40(1), 198–211 (2007)zbMATHCrossRefGoogle Scholar
  17. 17.
    Maio, D., Maltoni, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2003)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alper Kanak
    • 1
    • 2
  • İbrahim Sog̃ukpınar
    • 2
  1. 1.National Research Institute of Electronics and Cryptology, TUBITAK-UEKAE, Kocaeli, Turkiye 
  2. 2.Gebze Institute of Technology, Kocaeli, Turkiye 

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