Comparing Binary Iris Biometric Templates Based on Counting Bloom Filters

  • Christian Rathgeb
  • Christoph Busch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

In this paper a binary biometric comparator based on Counting Bloom filters is introduced. Within the proposed scheme binary biometric feature vectors are analyzed and appropriate bit sequences are mapped to Counting Bloom filters. The comparison of resulting sets of Counting Bloom filters significantly improves the biometric performance of the underlying system. The proposed approach is applied to binary iris-biometric feature vectors, i.e. iris-codes, generated from different feature extractors. Experimental evaluations, which carried out on the CASIA-v3-Interval iris database, confirm the soundness of the presented comparator.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloom, B.: Space/time tradeoffs in hash coding with allowable errors. Communications of the ACM 13(7), 422–426 (1970)CrossRefMATHGoogle Scholar
  2. 2.
    Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding 110(2), 281–307 (2007)CrossRefGoogle Scholar
  3. 3.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  4. 4.
    Davida, G., Frankel, Y., Matt, B.: On enabling secure applications through off-line biometric identification. In: Proc. IEEE Symp. on Security and Privacy, pp. 148–157. IEEE (1998)Google Scholar
  5. 5.
    Fan, L., Cao, P., Almeida, J., Broder, A.Z.: Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking 8(3), 281–293 (2000)CrossRefGoogle Scholar
  6. 6.
    Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: The best bits in an iris code. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 964–973 (2009)CrossRefGoogle Scholar
  7. 7.
    ISO/IEC TC JTC1 SC37 Biometrics. ISO/IEC 19795-1:2006. Information Technology – Biometric Performance Testing and Reporting – Part 1: Principles and Framework. International Organization for Standardization and International Electrotechnical Committee (March 2006)Google Scholar
  8. 8.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing 13(6), 739–750 (2004)CrossRefGoogle Scholar
  9. 9.
    Masek, L.: Recognition of human iris patterns for biometric identification. Master’s thesis, University of Western Australia (2003)Google Scholar
  10. 10.
    Rathgeb, C., Uhl, A., Wild, P.: Shifting score fusion: On exploiting shifting variation in iris recognition. In: Proc. 26th ACM Symposium on Applied Computing, pp. 1–5. ACM (2011)Google Scholar
  11. 11.
    Rathgeb, C., Uhl, A., Wild, P.: Iris-biometric comparators: Exploiting comparison scores towards an optimal alignment under gaussian assumption. In: Proc. 5th Int’l Conf. on Biometrics, pp. 1–6. IEEE (2012)Google Scholar
  12. 12.
    Rathgeb, C., Uhl, A., Wild, P.: Iris Biometrics: From Segmentation to Template Security. Advances in Information Security, vol. 59. Springer (2012)Google Scholar
  13. 13.
    Ziauddin, S., Dailey, M.: Iris recognition performance enhancement using weighted majority voting. In: Proc. 15th Int’l Conf. on Image Processing, pp. 277–280. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Rathgeb
    • 1
  • Christoph Busch
    • 1
  1. 1.da/sec Biometrics and Internet Security Research Group, Hochschule DarmstadtDarmstadtGermany

Personalised recommendations