Skip to main content
SpringerLink
Log in

Iris Recognition Performance Under Extreme Image Compression

  • Reference work entry
Encyclopedia of Biometrics

  • 81 Accesses

Definition

The compressibility of images is usually gauged by their subjective appearance and by metrics for the amount of distortion that can be tolerated. In the context of biometrics, compressibility can be gauged objectively by measuring the impact of compression schemes on recognition performance compared to baseline performance. Standard biometric methodologies such as Receiver Operating Characteristic (ROC) curves are perfectly suited for measuring the impact of compression on performance. It is possible for performance actually to benefit from slight image compression, as has been seen both with fingerprint and iris recognition, because high frequency noise is the first thing lost; but at more severe levels, compression must become detrimental. For iris recognition, it is possible to compress images to as little as 2,000 bytes through a combination of methods including cropping, region-of-interest (ROI) isolation and JPEG2000 wavelet coding, while suffering only a little...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 449.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bradley, J., Brislawn, C., Hopper, T.: The FBI Wavelet/Scalar Quantization standard for grayscale fingerprint image compression. Proc. SPIE (Applications of Digital Image Processing XIX) 2847 (1996)

    Google Scholar 

  2. Daugman, J., Downing, C.: Effect of severe image compression on iris recognition performance. IEEE Trans. Inform. Forensics Secur. 3, 52–61 (2008)

    Article  Google Scholar 

  3. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    MathSciNet  MATH  Google Scholar 

  4. Kolmogorov, A.: Three approaches to the quantitative definition of information. Probl. Inform. Transm. 1, 4–7 (1965)

    MathSciNet  Google Scholar 

  5. Terzopoulos, D., Waters, K.: Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Trans. Pattern Anal. Mach. Intell 15, 569–579 (1993)

    Article  Google Scholar 

  6. Cappelli, R., Maio, D., Maltoni, D.: Synthetic fingerprint-image generation. Proc. Int. Conf. Pattern Recognit. 15, 475–478 (2000)

    Google Scholar 

  7. Cui, J., Wang, Y., Huang, J., Tan, T., Sun, Z.: An iris image synthesis method based on PCA and super-resolution. Proc. 17th Int. Conf. Pattern Recognit. 4, 471–474 (2004)

    Article  Google Scholar 

  8. Zuo, J., Schmid, N., Chen, X.: On generation and analysis of synthetic iris images. IEEE Trans. Inform. Forensics Secur. 2, 77–90 (2007)

    Article  Google Scholar 

  9. Daugman, J., Downing, C.: Epigenetic randomness, complexity, and singularity of human iris patterns. Proc. R. Soc. B Biol. Sci. 268, 1737–1740 (2001)

    Article  Google Scholar 

  10. Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14, 21–30 (2004)

    Article  Google Scholar 

  11. Rakshit, S., Monro, D.: An evaluation of image sampling and compression for human iris recognition. IEEE Trans. Inform. Forensics Secur. 2, 605–612 (2007)

    Article  Google Scholar 

  12. National Institute of Standards and Technology. Iris challenge evaluation. http://iris.nist.gov/ice/

  13. Wallace, G.: The JPEG still picture compression standard. Commun. ACM 34, 30–44 (1991)

    Article  Google Scholar 

  14. International Organisation for Standards: Information technology – Digital compression and coding of continuous-tone still images. ISO/IEC 10918 (1994)

    Google Scholar 

  15. Bradley, A., Stentiford, F.: JPEG2000 and region of interest coding. In: Digital Imaging Computing Techniques and Applications. Melbourne, Australia (2002)

    Google Scholar 

  16. International Organisation for Standards: Information technology – JPEG2000 image coding system. ISO/IEC 15444-1 (2004)

    Google Scholar 

  17. Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: an overview. IEEE Trans. Consum. Electron 46, 1103–1127 (2000)

    Article  Google Scholar 

  18. Hsu, R., Griffin, P.: JPEG region of interest compression for face recognition. IDENTIX Doc. RDNJ-04-0102 (2005)

    Google Scholar 

  19. Registered Traveler Interoperability Consortium (RTIC): Technical Interoperability Specification for the Registered Traveler Program. http:///www.rtconsortium.org/

Download references

Author information

Authors and Affiliations

  1. Computer Laboratory, Cambridge University, Cambridge, UK

    John Daugman & Cathryn Downing

Authors
  1. John Daugman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cathryn Downing
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Biometrics and Security Research, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li (Professor) (Professor)

  2. Departments of Computer Science & Engineering, Michigan State University, East Lansing, MI, USA

    Anil Jain (Professor) (Professor)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Daugman, J., Downing, C. (2009). Iris Recognition Performance Under Extreme Image Compression. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_164

Download citation

Publish with us

Policies and ethics

Search

Navigation