Skip to main content
SpringerLink
Log in

Robust Fake Iris Detection

  • Conference paper
Articulated Motion and Deformable Objects (AMDO 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4069))

Included in the following conference series:

Abstract

Among biometrics such as face, fingerprint, iris and voice recognition, iris recognition system has been in the limelight for high security applications. Until now, most researches have been studied for iris identification algorithm and iris camera system, etc. But, there has been little researched for fake iris (such as printed, photographed or artificial iris, etc) detection and its importance has been much emphasized, recently. To overcome the problems of previous fake iris detection researches, we propose the new method of checking the hippus movement (the dilation/contraction of pupil size) and the change of iris code in local iris area by visible light in this paper.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Daugman, J.G.: High confidence visual recognition of personals by a test of statistical independence. IEEE Trans. Pattern Anal. Machine Intell. 15(11), 1148–1160 (1993)

    Article  Google Scholar 

  2. http://www.iris-recognition.org

  3. Jack, K.: Video Demystified. Harris (1996)

    Google Scholar 

  4. Jain, A.K.: Biometrics: Personal Identification in Networked Society. Kluwer academic publishers, Dordrecht (1998)

    Google Scholar 

  5. Smart Cards and Biometrics in Privacy-Sensitve Secure Personal Identification Systems. A Smart Card Alliance White Paper (May 2002)

    Google Scholar 

  6. Jain, R.: Machine Vision. McGraw-Hill International Edition, New York (1995)

    Google Scholar 

  7. Mansfield, T., et al.: Biometric Product Testing Final Report, Draft 0.6, National Physical Laboratory (March 2001)

    Google Scholar 

  8. Chapra, S.C., Canale, R.P.: Numerical Methods for Engineers. McGraw-Hill International Editions, New York (1989)

    Google Scholar 

  9. Gonzalez, R.C., et al.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  10. Ioammou, D., Huda, W., Laine, A.F.: Circle Recognition through a 2D Hough transform and Radius Histogramming. Image and Vision Computing 17, 15–26 (1999)

    Article  Google Scholar 

  11. Park, K.R., Lee, J.J., Kim, J.H.: Facial and Eye Gaze Detection. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 368–376. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Park, K.R., et al.: Gaze Point Detection by Computing the 3D Positions and 3D Motions of Face. IEICE Trans. Inf. & Syst. E.83-D(4), 884–894 (2000)

    Google Scholar 

  13. Park, K.R.: Gaze Detection by Estimating the Depth and 3D Motions of Facial Features in Monocular Images. IEICE Trans. Fund. E.82-A(10), 2274–2284 (1999)

    Google Scholar 

  14. Chapra, S.C., et al.: Numerical Methods for Engineers. McGraw-Hill, New York (1989)

    Google Scholar 

  15. http://www.heise.de/ct/english/02/11/114/

  16. Daugman, J.: Demodulation by complex-valued wavelets for stochastic pattern recognition. Int’l Journal of Wavelets, Multi-resolution and Information Processing 1(1), 1–17 (2003)

    Article  MATH  Google Scholar 

  17. Vogel, et al.: Optical Properties of Human Sclera and Their Consequences for Trans-scleral Laser Applications. Lasers in Surgery and Medicine 11(4), 331–340 (1991)

    Article  Google Scholar 

  18. Deng, J., et al.: Region-based Template Deformation and Masking for Eye Feature Extraction and Description. Pattern Recognition 30(3), 403–419 (1997)

    Article  Google Scholar 

  19. Kee, G., Byun, Y., Lee, K., Lee, Y.: Improved Technique for an Iris Recognition System with High Performance. In: AI 2001: Advances in Artificial Intelligence, pp. 177–188 (2001)

    Google Scholar 

  20. Mallet, S.G.: A Theory for Multi-resolution Signal Decomposition: The Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11(4), 674–693 (1989)

    Article  Google Scholar 

  21. Learned, R.E., Karl, W.C., Willsky, A.S.: Wavelet Packet based on Transient Signal Classification. In: Proc. of IEEE Conference on Time Scale and Time Frequency Analysis, pp. 109–112 (1992)

    Google Scholar 

  22. Jang, J., Park, K.R., Son, J., Lee, Y.: Multi-unit Iris Recognition System by Image Check Algorithm. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 450–457. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  23. Vapnik: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  24. Vapnik: Statistical Learning Theory. Wiley-Interscience publication, Chichester (1998)

    MATH  Google Scholar 

  25. Saunders: Support Vector Machine User Manual, RHUL, Technical Report (1998)

    Google Scholar 

  26. Daugman, J.: Biometric Decision Landscape, Technical Report No. TR482, University of Cambridge Computer Laboratory (2000)

    Google Scholar 

  27. Matsushita: Iris Image Capturing Device and Iris Image Authentication Device, Japanese Patent (Issued Number : 2002-247529)

    Google Scholar 

  28. Lee, E.C., Park, K.R., Kim, J.H.: Fake Iris Detection by Using Purkinje Image. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 397–403. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Park, K.R.: New automated iris image acquisition method. Applied Optics 44(5), 713–734 (2005)

    Article  Google Scholar 

  30. Ma, L., et al.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. on PAMI 25(12), 1519–1533 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Media Technology, Biometrics Engineering Research Center, Sangmyung University, 7 Hongji-Dong, Jongro-ku, Seoul, Republic of Korea

    Kang Ryoung Park

Authors
  1. Kang Ryoung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco J. Perales

  2. School of Informatics, University of Edinburgh, James Clerk Maxwell Building, The King’s Buildings, Mayfield Road, EH9 3JZ, Edinburgh, UK

    Robert B. Fisher

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Park, K.R. (2006). Robust Fake Iris Detection. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_2

Download citation

  • DOI: https://doi.org/10.1007/11789239_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36031-5

  • Online ISBN: 978-3-540-36032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation