Advertisement

On Some Performance Indices for Biometric Identification System

  • Jay Bhatnagar
  • Ajay Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

This paper investigates a new approach to formulate performance indices of biometric system using information theoretic models. The performance indices proposed here (unlike conventionally used FAR, GAR, DET etc.) are scalable in estimating performance of large scale biometric system. This work proposes a framework for identification capacity of a biometric system, along with insights on number of cohort users, capacity enhancements from user specific statistics etc. While incorporating feature level information in a rate-distortion framework, we derive condition for optimal feature representation. Furthermore, employing entropy measures to distance (hamming) distribution of the encoded templates, this paper proposes an upper bound for false random correspondence probability. Our analysis concludes that capacity can be the performance index of a biometric system while individuality expressed in false random correspondence can be the performance index of the biometric trait and representation. This paper also derives these indices and quantifies them from system parameters.

Keywords

Identification capacity Joint source-channel coding Individuality FRC (false random correspondence probability) 

References

  1. 1.
    Jain, A.K., Pankanti, S., Prabhakar, S., Hong, L., Ross, A., Wayman, J.L.: Biometrics: A Grand Challenge. In: Proc. ICPR, UK, vol. II, pp. 935–942 (2004)Google Scholar
  2. 2.
    Gallager, R G.: Information Theory and Reliable Communication. John Wiley, Chichester (1968)zbMATHGoogle Scholar
  3. 3.
    Jain, A.K., Ross, A., Pankanti, S.: Biometrics: A Tool for Information Security. IEEE Trans. Information Forensics and Security 1(2), 125–143 (2006)CrossRefGoogle Scholar
  4. 4.
    Adler, A., Youmaran, R., Loyka, S.: Towards a Measure of Biometric Information (February 2006), http://www.sce.carleton.ca/faculty/adler//publications
  5. 5.
    Slepian, D.: Key Papers in the Development of Information Theory. IEEE Press, New York (1974)zbMATHGoogle Scholar
  6. 6.
    Vembu, S., Verdú, S., Steinberg, Y.: The Source-Channel Separation Theorem Revisited. IEEE Trans. Information Theory 41(1), 44–54 (1995)zbMATHCrossRefGoogle Scholar
  7. 7.
    Simon, M., Hinedi, S., Lindsey, W.: Digital communication Techniques: Signal Design and Detection. Prentice-Hall, NJ (1995)Google Scholar
  8. 8.
    Feller, W.: An Introduction to Probability Theory and Applications. John Wiley & Sons, Chichester (1971)zbMATHGoogle Scholar
  9. 9.
    Aggarwal, G., Ratha, N., Bolle, R.M.: Biometric Verification: Looking Beyond Raw Similarity Scores. In: Workshop on Biometrics (CVPR), New York, pp. 31–36 (2006)Google Scholar
  10. 10.
    Pankanti, S., Prabhakar, S., Jain, A.K.: On the Individuality of Fingerprints. IEEE Trans. PAMI 24(8), 1010–1025 (2002)Google Scholar
  11. 11.
    Daugman, J.: Probing the uniqueness and randomness of Iris Codes: Results from 200 billion iris pair comparisons. Proc. of the IEEE 94(11), 1927–1935 (2006)CrossRefGoogle Scholar
  12. 12.
    Cover, T M., Thomas, J A.: Elements of Information Theory. John Wiley & Sons, Chichester (1991)zbMATHGoogle Scholar
  13. 13.
    Kumar, A., Zhang, D.: Feature selection and combination in biometrics. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 813–822. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jay Bhatnagar
    • 1
  • Ajay Kumar
    • 1
  1. 1.Biometrics Research Laboratory, Department of Electrical Engineering, Indian Institute of Technology Delhi, New DelhiIndia

Personalised recommendations