http://iet.metastore.ingenta.com
1887

Dilation-aware enrolment for iris recognition

Dilation-aware enrolment for iris recognition

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Iris recognition systems typically enrol a person based on a single ‘best’ eye image. Research has shown that the probability of a false non-match result increases with increased difference in pupil dilation between the enrolment image and the probe image. Therefore, dilation-aware methods of enrolment should improve the accuracy of iris recognition. The authors examine a strategy to improve accuracy through a dilation-aware enrolment step that selects one or more enrolment images based on the observed distribution of dilation ratios for that eye. Additionally, they demonstrate that an image with median dilation is the optimal single eye image dilation-aware enrolment choice. Their results confirm that this dilation-aware enrolment strategy does improve matching accuracy compared with traditional single-image enrolment, and also compared with multi-image enrolment that does not take dilation into account.

References

    1. 1)
      • 1. Flom, L., Safir, A.: ‘Iris recognition system’. U.S. Patent No. 4641349, U.S. Government Printing Office, Washington, DC, 1987.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. Grother, P., Tabassi, E., Quinn, G., et al: ‘IREX I: performance of iris recognition algorithms on standard images’. NIST Interagency Report, 7629, National Institute of Standards and Technology, October 2009.
    7. 7)
    8. 8)
    9. 9)
      • 9. Hollingsworth, K., Bowyer, K., Flynn, P.: ‘Image averaging for improved iris recognition’, in Tistarelli, M., Nixon, M. (EDs.): ‘Advances in biometrics’ (Springer, Berlin Heidelberg, 2009), vol. 5558of Lecture Notes in Computer Science, pp. 11121121.
    10. 10)
    11. 11)
      • 11. Du, Y.: ‘Using 2d log-gabor spatial filters for iris recognition’. Proc. of SPIE Biometric Technology for Human Identification III, 2006, vol. 6202, p. 62020F1.
    12. 12)
    13. 13)
    14. 14)
      • 14. ‘Samsung’. Available at http://www.samsung.com/, 2015. accessed 14 July 2015.
    15. 15)
      • 15. ‘SRI International to Offer Iris Biometric-Embedded Products for Mobile B2B Applications’. Available at http://www.sri.com/newsroom/press-releases/sri-international-offer-iris-biometric-embedded-products-mobile-b2b, 2015. accessed 14 July 2015.
    16. 16)
      • 16. Ortiz, E., Bowyer, K.: ‘Dilation aware multi-image enrollment for iris biometrics’. Int. Joint Conf. on Biometrics (IJCB), October 2011, pp. 17.
    17. 17)
      • 17. Liu, X., Bowyer, K.W., Flynn, P.: ‘Experiments with an improved iris segmentation algorithm’. Fourth IEEE Workshop on Automatic Identification Technologies, October 2005, pp. 118123.
    18. 18)
      • 18. ‘Neurotechnology’. Available at http://www.neurotechnology.com/verieye.html/, 2014. accessed 30 May 2014.
    19. 19)
      • 19. Ortiz, E., Bowyer, K., Flynn, P.: ‘An optimal strategy for dilation based iris image enrollment’. IEEE Int. Joint Conf. on Biometrics, September 2014, pp. 16.
    20. 20)
      • 20. Yuan, X., Shi, P.: ‘A non-linear normalization model for iris recognition’, in Li, S., Sun, Z., Tan, T., et al(EDs.): ‘Advances in biometric person authentication’ (Springer, Berlin Heidelberg, 2005), vol. 3781of Lecture Notes in Computer Science, pp. 135141.
    21. 21)
    22. 22)
      • 22. ‘IrisGuard’. Available at http://www.irisguard.com/, 2013, accessed 30 May 2013.
    23. 23)
      • 23. Ahsanullah, M., Nevzorov, V.: ‘Order statistics: examples and exercises’ (Nova Science Pub Inc., 2004).
    24. 24)
      • 24. David, H., Nagaraja, H.: ‘Order statistics’ (John Wiley and Sons, 2003).
    25. 25)
      • 25. ‘The University of Notre Dame Computer Vision Research Lab’. Available at http://www3.nd.edu/~cvrl/CVRL/DataSets.html, 2015, accessed: 14 July 2015.
    26. 26)
    27. 27)
      • 27. Ortiz, E., Bowyer, K., Flynn, P.: ‘A linear regression analysis of the effects of age related pupil dilation change in iris biometrics’. Sixth IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS), October 2013, pp. 16.
    28. 28)
      • 28. Casella, G., Berger, R.L.: ‘Statistical inference’ (Duxbury Press Belmont, CA, 1990), vol. 70.
    29. 29)
      • 29. Gillard, J.: ‘An overview of linear structural models in errors in variables regression’, REVSTAT–Stat. J., 2010, 8, (1), pp. 5780.
    30. 30)
      • 30. Van Trees, H.L.: ‘Detection, estimation, and modulation theory’ (John Wiley & Sons, 2004).
    31. 31)
      • 31. Kay, S.M.: ‘Fundamentals of statistical signal processing: detection theory’ (Prentice-Hall, 1998).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2015.0005
Loading

Related content

content/journals/10.1049/iet-bmt.2015.0005
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address