Comparison of Compression Algorithms’ Impact on Iris Recognition Accuracy

  • Stefan Matschitsch
  • Martin Tschinder
  • Andreas Uhl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

The impact of using different lossy compression algorithms on the matching accuracy of iris recognition systems is investigated. In particular, we relate rate-distortion performance as measured in PSNR to the matching scores as obtained by a concrete recognition system. JPEG2000 and SPIHT are correctly predicted by PSNR to be well suited compression algorithms to be employed in iris recognition systems. Fractal compression is identified to be least suited for the use in the investigated recognition system, although PSNR suggests JPEG to deliver worse recognition results in the case of low bitrates. PRVQ compression performs surprisingly well given the third rank in PSNR performance, resulting in the best matching scores in one scenario. Overall, applying compression algorithms is found to increase FNMR but does not impact FMR. Consequently, compression does not decrease the security of iris recognition systems, but “only” reduces user convenience.

Keywords

Recognition Accuracy Image Compression Compression Algorithm Iris Image Compression 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.

References

  1. 1.
    Pennebaker, W., Mitchell, J.: JPEG – Still image compression standard. Van Nostrand Reinhold, New York (1993)Google Scholar
  2. 2.
    Taubman, D., Marcellin, M.: JPEG2000 — Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  3. 3.
    Jerabek, B., Schneider, P., Uhl, A.: Comparison of lossy image compression methods applied to photorealistic and graphical images using public domain sources. Tech. Rep. RIST++15/98, Research Institute for Softwaretechnology, University of Salzburg (1998)Google Scholar
  4. 4.
    Funk, W., Arnold, M., Busch, C., Munde, A.: Evaluation of image compression algorithms for fingerprint and face recognition systems. In: Cole, J., Wolthusen, S. (eds.) Proceedings from the Sixth Annual IEEE Systems, Man and Cybernetics (SMC) Information Assurance Workshop, June 2006, pp. 72–78. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  5. 5.
    Said, A., Pearlman, W.A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 6, 243–249 (1996)CrossRefGoogle Scholar
  6. 6.
    Delac, K., Grigic, M., Grigic, 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
  7. 7.
    McGarry, D., Arndt, C., McCabe, S., D’Amato, D.: Effects of compression and individual variability on face recognition performance. In: Biometric Technology for Human Identification. Proceedings of SPIE, vol. 5404, pp. 362–372 (August 2004)Google Scholar
  8. 8.
    Mascher-Kampfer, A., Stögner, H., Uhl, A.: Comparison of Compression Algorithms’ Impact on Fingerprint and Face Recognition Accuracy. In: Visual Computing and Image Processing VCIP 07. Proceedings of SPIE, vol. 6508, pp. 65080N-1–65050N-10 (January 2007)Google Scholar
  9. 9.
    Rakshit, S., Monro, D.M.: Effects of Sampling and Compression on Human Iris Verification. In: Proceedings of the IEEE International Conference on Acustics, Speech, and Signal Processing ICASSP 2006. IEEE Signal Processing Society, pp. II-337–II-340 (July 2007)Google Scholar
  10. 10.
    Ives, R.W., lBonney, B.L., Etter, D.M.: Effect of image compression on iris recognition. In: IMTC 2005 – Instrumentation and Measurement Technology Conference (May 2005)Google Scholar
  11. 11.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circiuts and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  12. 12.
    von Seelen, U.: IrisCode template compression and its effects on authentication performance. In: Biometrics Consortium Conference 2003 (September 2003)Google Scholar
  13. 13.
    Beleznai, C., Ramoser, H., Wachmann, B., Birchbauer, J., Bischof, H., Kropatsch, W.: Memory-efficient fingerprint verification. In: ICIP 2001. Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece, vol. 2, pp. 463–466 (October 2001)Google Scholar
  14. 14.
    Fisher, Y. (ed.): Fractal Image Compression: Theory and Application. Springer, New York (1995)Google Scholar
  15. 15.
    Masek, L., Kovesi, P.: MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Stefan Matschitsch
    • 1
  • Martin Tschinder
    • 1
  • Andreas Uhl
    • 1
    • 2
  1. 1.School of Telematics & Network Engineering, Carinthia Tech InstituteAustria
  2. 2.Department of Computer Sciences, Salzburg UniversityAustria

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