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Journal of Electrical and Computer Engineering
Volume 2012 (2012), Article ID 282589, 9 pages
http://dx.doi.org/10.1155/2012/282589
Research Article

Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada K1S 5B6

Received 5 February 2012; Revised 21 May 2012; Accepted 22 May 2012

Academic Editor: Weiyao Lin

Copyright © 2012 R. Youmaran and A. Adler. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. S. Wayman, “The cotton ball problem,” in Proceedings of the Biometrics Conference, Washington, DC, USA, September 2004.
  2. J. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recognition, vol. 36, no. 2, pp. 279–291, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. T. M. Cover and A. J. Thomas, Elements of Information Theory, John Wiley & Sons, New York, NY, USA, 1991.
  4. A. Ross and A. Jain, “Information fusion in biometrics,” Pattern Recognition Letters, vol. 24, no. 13, pp. 2115–2125, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Adler, R. Youmaran, and S. Loyka, “Towards a measure of biometric feature information,” Pattern Analysis and Applications, vol. 12, no. 3, pp. 261–270, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Masek, Recognition of human iris patterns for biometric identification [M.S. thesis], School of Computer Science and Software Engineering, University of West Australia, Perth, Australia, 2003.
  7. L. Masek and P. Kovesi, MATLAB Source Code for a Biometric Identification System Based on Iris Patterns, School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia, 2003.
  8. J. Wayman, A. Jain, D. Maltoni, and D. Maio, Biometric Systems: Technology, Design and Performance Evaluation, Springer, London, UK, 2005.
  9. E. M. Newton and P. J. Phillips, “Meta-analysis of third-party evaluations of iris recognition,” Technical Report NISTIR 7440, National Institute of Standards and Technology, Gaithersburg, Md, USA, 2007.
  10. R. P. Wildes, “Iris recognition: an emerging biometrie technology,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1348–1363, 1997. View at Scopus
  11. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993. View at Publisher · View at Google Scholar · View at Scopus
  12. H. P. Proenca, Towards non-cooperative biometric iris recognition [Ph.D. thesis], University of Beira Interior, Department of Computer Science, 2006.
  13. W. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 1185–1188, 1998. View at Scopus
  14. N. Brady and D. J. Field, “Local contrast in natural images: normalisation and coding efficiency,” Perception, vol. 29, no. 9, pp. 1041–1055, 2000. View at Scopus
  15. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” Journal of the Optical Society of America A, vol. 4, no. 12, pp. 2379–2394, 1987. View at Scopus
  16. B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with PCA and ICA,” Computer Vision and Image Understanding, vol. 91, no. 1-2, pp. 115–137, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Grother, “Software Tools for an Eigenface Implementation,” National Institute of Standards and Technology, 2000, http://www.nist.gov/humanid/feret/.
  18. O. Alter, P. O. Brown, and D. Botstein, “Singular value decomposition for genome-wide expression data processing and modeling,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 18, pp. 10101–10106, 2000. View at Scopus
  19. “Casia iris image database,” Chinese Academy of Sciences Institute of Automation, 2004, http://www.sinobiometrics.com.
  20. C. Xiang, X. A. Fan, and T. H. Lee, “Face recognition using recursive Fisher linear discriminant,” in Proceedings of the International Conference on Communications, Circuits and Systems (ICCCAS '04), vol. 2, pp. 800–804, June 2004. View at Scopus
  21. J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds, “Sheep, goats, lambs and wolves: an analysis of individual differences in speaker recognition performance,” in Proceedings of the International Conference on Auditory Visual Speech Processing, Sidney, Australia, November 1998.
  23. S. Pankanti, S. Prabhakar, and A. K. Jain, “On the individuality of fingerprints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1010–1025, 2002. View at Publisher · View at Google Scholar · View at Scopus