Skip to main content
SpringerLink
Log in

Automatic Classification of Left/Right Iris Image

  • Reference work entry
  • First Online:
Encyclopedia of Biometrics

  • 137 Accesses

Synonyms

Mislabeled iris data correction

Definition

Many iris acquisition devices capture irises from a single eye at a time. The device operator must typically enter metadata such as name, address, and which eye by hand. In many deployment scenarios, it is easy for the device operator to be distracted and mislabel the eyes. Such mislabeling can pose serious problems for database indexing. In this entry, the authors describe an extremely efficient algorithm for automatic classification of eyes into left/right categories. This algorithm makes use of the iris/pupil segmentation information that is already computed for most iris recognition algorithms, so it poses a minimal computational load and requires minimal modifications to existing iris recognition systems.

Introduction

Iris recognition is generally considered to be one of the most effective biometric modalities for biometric identification [1]. Iris is a good biometric because (1) the iris is rich in texture and that texture has...

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 899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. A.K. Jain, A. Ross, S. Prabhakar, An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Tech. 14(1), 4–20 (2004)

    Google Scholar 

  2. J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. video Tech. 14(1), 21–30 (2004)

    Google Scholar 

  3. R. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE 85, 1348–1363 (1997)

    Google Scholar 

  4. Y.P. Huang, S.W. Luo, E.Y. Chen, An efficient iris recognition system. Int. Conf. Mach. Learn. Cybern. 1, 450–454 (2002)

    Google Scholar 

  5. Y. Liu, S. Yuan, X. Zhu, Q. Cui, A practical iris acquisition system and a fast edges locating algorithm in iris recognition, in IEEE Instrumentation and Measurement Technology Conference, Vail, vol. 26, 2003, pp. 166–168

    Google Scholar 

  6. H. Sung, J. Lim, J.H. Park, Y. Lee, Iris recognition using collarette boundary localization, in International Conference on Pattern Recognition, Cambridge, 2004, vol. IV, pp. 857–860

    Google Scholar 

  7. L. Masek, Recognition of human iris patterns for biometric identification. Master thesis, University of Western Australia, 2003. Available from: http://www.csse.uwa.edu.au/verb++pk/studentprojects/libor/

    Google Scholar 

  8. X. Liu, K.W. Bowyer, P.J. Flynn, Experiments with an improved iris segmentation algorithm, in Fourth IEEE Workshop on Automatic Identification Technologies, Buffalo, 2005, vol. 113, pp. 118–123

    Google Scholar 

  9. L. Pan, M. Xie, The algorithm of iris image processing, in Fourth IEEE Workshop on Automatic Identification Technologies, Buffalo, 2005, pp. 134–138

    Google Scholar 

  10. X.F. He, P.F. Shi, A novel iris segmentation method for hand-held capture device, in International Conference on Biometrics. LNCS, vol. 3832, Hong Kong (Springer, 2006), pp. 479–485

    Google Scholar 

  11. X. Feng, C. Fang, Z. Ding, Y. Wu, Iris localization with dual coarse-to-fine strategy, in International Conference on Pattern Recognition, Hong Kong, 2006, pp. 553–556

    Google Scholar 

  12. National Institute of Standards and Technology, Iris challenge evaluation 2005 workshop presentations. Available from: http://iris.nist.gov/ice/presentations.htm. Last Accessed 24 Mar 2009

  13. J.R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, W.Y. Zhao, Iris on the move: acquisition of images for iris recognition in less constrained environments. Proc. IEEE 94(11), 1936–1946 (2006)

    Google Scholar 

  14. J. Thornton, M. Savvides, Vijaya Kumar, B.V.K., A bayesian approach to deformed pattern matching of iris images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 596–556 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Language Technology Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Yung-hui Li

  2. CMU CyLab Biometrics Center, Pittsburgh, PA, USA

    Marios Savvides

Authors
  1. Yung-hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marios Savvides
    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 & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

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

    Anil K. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Li, Yh., Savvides, M. (2015). Automatic Classification of Left/Right Iris Image. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_220

Download citation

Publish with us

Policies and ethics

Search

Navigation