A Hardware-Implemented Truly Random Key Generator for Secure Biometric Authentication Systems

  • Murat Erat
  • Kenan Danışman
  • Salih Ergün
  • Alper Kanak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


Recent advances in information security requires strong keys which are randomly generated. Most of the keys are generated by the softwares which use software-based random number generators. However, implementing a True Random Number Generator (TRNG) without using a hardware-supported platform is not reliable. In this paper, a biometric authentication system using a FPGA-based TRNG to produce a private key that encrypts the face template of a person is presented. The designed hardware can easily be mounted on standard or embedded PC via its PCI interface to produce random number keys. Random numbers forming the private key is guaranteed to be true because it passes a two-level randomness test. The randomness test is evaluated first on the hardware then on the PC by applying the full NIST test suite. The whole system implements an AES-based encryption scheme to store the person’s secret safely. Assigning a private key which is generated by our TRNG guarantees a unique and truly random password. The system stores the Wavelet Fourier-Mellin Transform (WFMT) based face features in a database with an index number that might be stored on a smart or glossary card. The objective of this study is to present a practical application integrating any biometric technology with a hardware-implemented TRNG.


Face Image Random Number Generator Authentication Scheme Advance Encryption Standard Data Encryption Standard 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jun, B., Kocher, P.: The Intel Random Number Generator. Cryptography Research, Inc. white paper prepared for Inter Corp. (1999), http://www.cryptography.com/resources/whitepapers/IntelRNG.pdf
  2. 2.
    Menezes, A., Oorschot, P., van Vanstone, S.: Handbook of Applied Cryptology. CRC Press, Boca Raton (1996)CrossRefGoogle Scholar
  3. 3.
    Schneier, B.: Applied Cryptography, 2nd edn. John Wiley & Sons, Chichester (1996)Google Scholar
  4. 4.
    Holman, W.T., Connelly, J.A., Downlatabadi, A.B.: An Integrated Analog-Digital Random Noise Source. IEEE Trans. Circuits & Systems I 44(6), 521–528 (1997)CrossRefGoogle Scholar
  5. 5.
    Bagini, V., Bucci, M.: A Design of Reliable True Random Number Generator for Cryptographic Applications. In: Koç, Ç.K., Paar, C. (eds.) CHES 1999. LNCS, vol. 1717, pp. 204–218. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Dichtl, M., Janssen, N.: A High Quality Physical Random Number Generator. In: Proc. Sophia Antipolis Forum Microelectronics (SAME), pp. 48–53 (2000)Google Scholar
  7. 7.
    Petrie, C.S., Connelly, J.A.: Modeling and Simulation of Oscillator-Based Random Number Generators. In: Proc. IEEE Int. Symp. on Circuits & Systems (ISCAS), vol. 4, pp. 324–327 (1996)Google Scholar
  8. 8.
    Stojanovski, T., Kocarev, L.: Chaos-Based Random Number Generators-Part I: Analysis. IEEE Trans. Circuits & Systems I 48(3), 281–288 (2001)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Delgado-Restituto, M., Medeiro, F., Rodriguez-Vazquez, A.: Nonlinear Switched-current CMOS IC for Random Signal Generation. Electronics Letters 29(25), 2190–2191 (1993)CrossRefGoogle Scholar
  10. 10.
    Yalcin, M.E., Suykens, J.A.K., Vandewalle, J.: True Random Bit Generation from a Double Scroll Attractor. IEEE Trans. on Circuits & Systems I: Fundamental Theory and Applications 51(7), 1395–1404 (2004)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Teoh, A.B.J., Ngo, D.C.L., Goh, A.: Personalised Cryptographic Key Generation Based on FaceHashing. Jour. of Computer & Security (2004)Google Scholar
  12. 12.
    Von Neumann, J.: Various Techniques Used in Connection With Random Digits. In: Forsythe, G.E. (ed.) National Bureau of Standards. Applied Math. Series, vol. 12, pp. 36–38 (1951)Google Scholar
  13. 13.
    National Institute of Standard and Technology. A Statistical Test Suite for Random and Pseudo Random Number Generators for Cryptographic Applications. NIST 800-22 (2001), http://csrc.nist.gov/rng/SP800-22b.pdf
  14. 14.
    Samaria, F., Harter, A.: Parameterisation of a Stochastic Model for Human Face Identification. In: 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL (December 1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Murat Erat
    • 1
    • 2
  • Kenan Danışman
    • 2
  • Salih Ergün
    • 1
  • Alper Kanak
    • 1
  1. 1.TÜBİTAK-National Research Institute of Electronics and CryptologyGebze, KocaeliTurkiye
  2. 2.Dept. of Electronics EngineeringErciyes UniversityKayseriTurkiye

Personalised recommendations