Dilation-aware enrolment for iris recognition

Estefan Ortiz; Kevin W. Bowyer; Patrick J. Flynn

Dilation-aware enrolment for iris recognition

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Author(s): Estefan Ortiz ¹ ; Kevin W. Bowyer ¹ ; Patrick J. Flynn ¹
- Affiliations: 1: Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA
Source: Volume 5, Issue 2, June 2016, p. 92 – 99
DOI: 10.1049/iet-bmt.2015.0005 , Print ISSN 2047-4938, Online ISSN 2047-4946

Received 31/01/2015, Accepted 11/08/2015, Revised 23/07/2015, Published 01/06/2016

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)
  - 23. Ahsanullah, M., Nevzorov, V.: ‘Order statistics: examples and exercises’ (Nova Science Pub Inc., 2004).
2. 2)
  - 23. Ma, L., Tan, T., Wang, Y., Zhang, D.: ‘Efficient iris recognition by characterizing key local variations’, IEEE Trans. Image Process., 2004, 13, (6), pp. 739–750 (doi: 10.1109/TIP.2004.827237).
3. 3)
  - 22. ‘IrisGuard’. Available at http://www.irisguard.com/, 2013, accessed 30 May 2013.
4. 4)
  - 30. Van Trees, H.L.: ‘Detection, estimation, and modulation theory’ (John Wiley & Sons, 2004).
5. 5)
  - 5. Hollingsworth, K., Bowyer, K., Flynn, P.: ‘Pupil dilation degrades iris biometric performance’, Comput. Vis. Image Underst., 2009, 113, (1), pp. 150–157 (doi: 10.1016/j.cviu.2008.08.001).
6. 6)
  - 8. Matey, J., Naroditsky, O., Hanna, K., et al: ‘Iris on the move: acquisition of images for iris recognition in less constrained environments’, Proc. IEEE, 2006, 94, pp. 1936–1947 (doi: 10.1109/JPROC.2006.884091).
7. 7)
  - 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. 135–141.
8. 8)
  - 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.
9. 9)
  - 13. Schmid, N., Ketkar, M., Singh, H., et al: ‘Performance analysis of iris-based identification system at the matching score level’, IEEE Trans. Inf. Forensics Sec., 2006, 1, pp. 154–168 (doi: 10.1109/TIFS.2006.873603).
10. 10)
  - 21. Clark, A., Kulp, S., Herron, I., et al: ‘A theoretical model for describing iris dynamics’, Handb. Iris Recognit., 2013, pp. 129–150 (doi: 10.1007/978-1-4471-4402-1_7).
11. 11)
  - 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. 1–6.
12. 12)
  - 14. ‘Samsung’. Available at http://www.samsung.com/, 2015. accessed 14 July 2015.
13. 13)
  - 9. Fairhurst, M., Erbilek, M.: ‘Analysis of physical ageing effects in iris biometrics’, IET Comput. Vis., 2011, 5, (6), pp. 358–366 (doi: 10.1049/iet-cvi.2010.0165).
14. 14)
  - 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. 118–123.
15. 15)
  - 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. 1112–1121.
16. 16)
  - 3. Daugman, J.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, pp. 21–30 (doi: 10.1109/TCSVT.2003.818350).
17. 17)
  - 27. Daugman, J.: ‘New methods in iris recognition’, IEEE Trans. Syst. Man Cybern. B, 2007, 37, (5), pp. 1167–1175 (doi: 10.1109/TSMCB.2007.903540).
18. 18)
  - 24. David, H., Nagaraja, H.: ‘Order statistics’ (John Wiley and Sons, 2003).
19. 19)
  - 10. Pillai, J., Patel, V., Chellappa, R., et al: ‘Secure and robust iris recognition using random projections and sparse representations’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, pp. 1877–1893 (doi: 10.1109/TPAMI.2011.34).
20. 20)
  - 2. Daugman, J.: ‘High confidence visual recognition of persons by a test of statistical independence’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, pp. 1148–1161 (doi: 10.1109/34.244676).
21. 21)
  - 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.
22. 22)
  - 31. Kay, S.M.: ‘Fundamentals of statistical signal processing: detection theory’ (Prentice-Hall, 1998).
23. 23)
  - 28. Casella, G., Berger, R.L.: ‘Statistical inference’ (Duxbury Press Belmont, CA, 1990), vol. 70.
24. 24)
  - 26. Fawcett, T.: ‘An introduction to ROC analysis’, Pattern Recognit. Lett., 2006, 27, pp. 861–874 (doi: 10.1016/j.patrec.2005.10.010).
25. 25)
  - 16. Ortiz, E., Bowyer, K.: ‘Dilation aware multi-image enrollment for iris biometrics’. Int. Joint Conf. on Biometrics (IJCB), October 2011, pp. 1–7.
26. 26)
  - 18. ‘Neurotechnology’. Available at http://www.neurotechnology.com/verieye.html/, 2014. accessed 30 May 2014.
27. 27)
  - 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. 62020F–1.
28. 28)
  - 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.
29. 29)
  - 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. 1–6.
30. 30)
  - 29. Gillard, J.: ‘An overview of linear structural models in errors in variables regression’, REVSTAT–Stat. J., 2010, 8, (1), pp. 57–80.
31. 31)
  - 1. Flom, L., Safir, A.: ‘Iris recognition system’. U.S. Patent No. 4641349, U.S. Government Printing Office, Washington, DC, 1987.

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