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Handbook of Iris Recognition pp 281–308Cite as

Iris Segmentation for Challenging Periocular Images

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

This chapter discusses the performance of five different iris segmentation algorithms on challenging periocular images. The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, defocus blur, motion blur, and low resolution. The five algorithms considered in this regard are based on the (a) integrodifferential operator, (b) Hough transform, (c) geodesic active contours, (d) active contours without edges, and (e) directional ray detection method. Experiments on the Face and Ocular Challenge Series (FOCS) database highlight the pros and cons of the individual segmentation algorithms.

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Notes

  1. 1.

    The subscript t denotes the iteration number.

References

  1. Bharadwaj, S., Bhatt, H., Vatsa, M., Singh, R.: Periocular biometrics: when iris recognition fails. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), BTAS’10. IEEE, Piscataway (2010)

    Google Scholar 

  2. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  3. Daugman, J.: How iris recognition works (2002)

    Google Scholar 

  4. Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vis. Graph. Image Process. 44(1), 87–116 (1988)

    Article  Google Scholar 

  5. Kumar, B.V.K.V., Hassebrook, L.: Performance measures for correlation filters. Appl. Opt. 29(20), 2997–3006 (1990)

    Article  Google Scholar 

  6. Kumar, B., et al.: Biometric verification with correlation filters. Appl. Opt. 43(2), pp. 391-402 (2004)

    Article  Google Scholar 

  7. Li, D., Babcock, J.S., Parkhurst, D.J.: Openeyes: a low-cost head-mounted eye-tracking solution. In: ETRA, San Diego, California, USA, pp. 95–100 (2006)

    Google Scholar 

  8. Matey, J., Naroditsky, O., Hanna, K., Kolczynski, R., LoIacono, D., Mangru, S., Tinker, M., Zappia, T., Zhao, W.: Iris On the Move: acquisition of images for iris recognition in less constrained environments. Proc. IEEE 94(11), 1936–1947 (2006)

    Article  Google Scholar 

  9. Miller, P., Lyle, J., Pundlik, S., Woodard, D.: Performance evaluation of local appearance based periocular recognition. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), BTAS’10. IEEE, Piscataway (2010)

    Google Scholar 

  10. Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum: a feasibility study. In: Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, applications and Systems, BTAS’09, pp. 153–158. IEEE, Piscataway (2009)

    Google Scholar 

  11. Park, U., Jillela, R.R., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. 6(1), 96–106 (2011)

    Article  Google Scholar 

  12. Ryan, W., Woodard, D., Duchowski, A., Birchfield, S.: Adapting starburst for elliptical iris segmentation. In: Proceedings of the 2rd IEEE International Conference on Biometrics: Theory, applications and Systems, BTAS’08. IEEE, Piscataway (2008)

    Google Scholar 

  13. Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. 4(4), 824–836 (2009)

    Article  Google Scholar 

  14. Sundaramoorthi, G., Soatto, S., Yezzi, A.J.: Curious snakes: a minimum latency solution to the cluttered background problem in active contours. In: CVPR, San Francisco, pp. 2855–2862 (2010)

    Google Scholar 

  15. Wildes, R., Asmuth, J., Green, G., Hsu, S., Kolczynski, R., Matey, J., McBride, S.: A system for automated iris recognition. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, Sarasota, pp. 121–128 (1994)

    Google Scholar 

  16. Woodard, D.L., Pundlik, S.J., Miller, P.E., Lyle, J.R.: Appearance-based periocular features in the context of face and non-ideal iris recognition. Signal Image Video Process. 5(4), 443–455 (2011)

    Article  Google Scholar 

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Acknowledgments

This work was sponsored under IARPA BAA 09-02 through the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2- 0013. The views and conclusions contained in this chapter are those of the authors and should not be interpreted as representing of official policies, either expressed or implied, of IARPA, the Army Research Laboratory, or the US government. The US government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein.

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Authors and Affiliations

  1. West Virginia University, Morgantown, WV, USA

    Raghavender Jillela & Arun A. Ross

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Vishnu Naresh Boddeti & B. V. K. Vijaya Kumar

  3. Wake Forest University, Winston-Salem, NC, USA

    Xiaofei Hu, Robert Plemmons & Paúl Pauca

Authors
  1. Raghavender Jillela
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  2. Arun A. Ross
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  3. Vishnu Naresh Boddeti
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  4. B. V. K. Vijaya Kumar
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  5. Xiaofei Hu
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  6. Robert Plemmons
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  7. Paúl Pauca
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Corresponding author

Correspondence to Raghavender Jillela .

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Editors and Affiliations

  1. MITRE Corporation, Colshire Drive 7515, McLean, 22102-7539, Virginia, USA

    Mark J. Burge

  2. , Department of Computer Science and Engin, University of Notre Dame, Fitzpatrick Hall 384, Notre Dame, 46556-5637, Indiana, USA

    Kevin W. Bowyer

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Jillela, R. et al. (2013). Iris Segmentation for Challenging Periocular Images. In: Burge, M., Bowyer, K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4402-1_14

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  • DOI: https://doi.org/10.1007/978-1-4471-4402-1_14

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4401-4

  • Online ISBN: 978-1-4471-4402-1

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