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Iris-Biometric Fuzzy Commitment Schemes under Image Compression

  • Christian Rathgeb
  • Andreas Uhl
  • Peter Wild
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

With the introduction of template protection techniques, privacy and security of biometric data have been enforced. Meeting the required properties of irreversibility, i.e. avoiding a reconstruction of original biometric features, and unlinkability among each other, template protection can enhance security of existing biometric systems in case tokens are stolen. However, with increasing resolution and number of enrolled users in biometric systems, means to compress biometric signals become an imminent need and practice, raising questions about the impact of image compression on recognition accuracy of template protection schemes, which are particularly sensitive to any sort of signal degradation. This paper addresses the important topic of iris-biometric fuzzy commitment schemes’ robustness with respect to compression noise. Experiments using a fuzzy commitment scheme indicate, that medium compression does not drastically effect key retrieval performance.

Keywords

Image Compression Biometric System Lossy Compression Iris Recognition Compression Level 
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.

References

  1. 1.
    Ao, M., Li, S.Z.: Near infrared face based biometric key binding. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 376–385. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Bringer, J., Chabanne, H., Cohen, G., Kindarji, B., Zémor, G.: Theoretical and practical boundaries of binary secure sketches. IEEE Trans. on Information Forensics and Security 3, 673–683Google Scholar
  3. 3.
    Cavoukian, A., Stoianov, A.: Biometric encryption: The new breed of untraceable biometrics. In: Biometrics: fundamentals, theory, and systems. Wiley (2009)Google Scholar
  4. 4.
    Daugman, J., Downing, C.: Effect of severe image compression on iris recognition performance. IEEE Trans. on Inf. Forensics and Sec. 3(1), 52–61 (2008)CrossRefGoogle Scholar
  5. 5.
    Delac, K., Grgic, M., Grgic, S.: Effects of JPEG and JPEG2000 compression on face recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 136–145. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Grother, P.: Quantitative standardization of iris image formats. In: Proc. of the Biometrics and Electronic Signatures (BIOSIG 2009). LNI, pp. 143–154 (2009)Google Scholar
  7. 7.
    Hämmerle-Uhl, J., Prähauser, C., Starzacher, T., Uhl, A.: Improving compressed iris recognition accuracy using JPEG2000 roI coding. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1102–1111. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Hao, F., Anderson, R., Daugman, J.: Combining Cryptography with Biometrics Effectively. IEEE Trans. on Computers 55(9), 1081–1088 (2006)CrossRefGoogle Scholar
  9. 9.
    Ives, R.W., Broussard, R.P., Kennell, L.R., Soldan, D.L.: Effects of image compression on iris recognition system performance. Journal of Electronic Imaging 17, 11015 (2008), doi:10.1117/1.2891313CrossRefGoogle Scholar
  10. 10.
    Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Signal Process 2008, 1–17 (2008)CrossRefGoogle Scholar
  11. 11.
    Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: Sixth ACM Conference on Computer and Communications Security, pp. 28–36 (1999)Google Scholar
  12. 12.
    Konrad, M., Stögner, H., Uhl, A.: Custom design of JPEG quantisation tables for compressing iris polar images to improve recognition accuracy. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1091–1101. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Kostmajer, G.S., Stögner, H., Uhl, A.: Custom JPEG quantization for improved iris recognition accuracy. In: Gritzalis, D., Lopez, J. (eds.) SEC 2009. IFIP AICT, vol. 297, pp. 76–86. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recogntion by Characterizing Key Local Variations. IEEE Trans. on Image Processing 13(6), 739–750 (2004)CrossRefGoogle Scholar
  15. 15.
    Maiorana, E., Campisi, P.: Fuzzy commitment for function based signature template protection. IEEE Signal Processing Letters 17, 249–252 (2010)CrossRefGoogle Scholar
  16. 16.
    Nandakumar, K.: A fingerprint cryptosystem based on minutiae phase spectrum. In: Proc. of IEEE Workshop on Information Forensics and Security (WIFS) (2010)Google Scholar
  17. 17.
    Rakshit, S., Monro, D.M.: An evaluation of image sampling and compression for human iris recognition. IEEE Trans. Inf. Forensics and Sec. 2, 605–612 (2007)CrossRefGoogle Scholar
  18. 18.
    Rathgeb, C., Uhl, A.: Adaptive fuzzy commitment scheme based on iris-code error analysis. In: Proc. of the 2nd Europ. Workshop on Visual Inf. Proc. (EUVIP 2010), pp. 41–44 (2010)Google Scholar
  19. 19.
    Rathgeb, C., Uhl, A., Wild, P.: Reliability-balanced feature level fusion for fuzzy commitment scheme. In: Int’l Joint Conf. on Biometrics, pp. 1–7 (2011)Google Scholar
  20. 20.
    Sherlock, B.G., Monro, D.M.: Optimized wavelets for fingerprint compression. In: Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1996), Atlanta, GA, USA (May 1996)Google Scholar
  21. 21.
    Teoh, A., Kim, J.: Secure biometric template protection in fuzzy commitment scheme. IEICE Electron. Express 4(23), 724–730 (2007)CrossRefGoogle Scholar
  22. 22.
    Van der Veen, M., Kevenaar, T., Schrijen, G.-J., Akkermans, T.H., Zuo, F.: Face biometrics with renewable templates. In: SPIE Proc. on Security, Steganography, and Watermarking of Multimedia Contents, vol. 6072, pp. 205–216 (2006)Google Scholar
  23. 23.
    Zhang, L., Sun, Z., Tan, T., Hu, S.: Robust biometric key extraction based on iris cryptosystem. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1060–1069. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Rathgeb
    • 1
  • Andreas Uhl
    • 2
  • Peter Wild
    • 2
  1. 1.Hochschule Darmstadt - CASEDDarmstadtGermany
  2. 2.Dept. of Computer SciencesUniversity of SalzburgAustria

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