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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 19 — Jul. 1, 2012
  • pp: 4275–4284

Optimal wavelength band clustering for multispectral iris recognition

Yazhuo Gong, David Zhang, Pengfei Shi, and Jingqi Yan  »View Author Affiliations


Applied Optics, Vol. 51, Issue 19, pp. 4275-4284 (2012)
http://dx.doi.org/10.1364/AO.51.004275


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Abstract

This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths–bands fusion.

© 2012 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(170.2945) Medical optics and biotechnology : Illumination design
(100.3005) Image processing : Image recognition devices
(230.7408) Optical devices : Wavelength filtering devices

ToC Category:
Optical Devices

History
Original Manuscript: December 8, 2011
Revised Manuscript: April 19, 2012
Manuscript Accepted: May 7, 2012
Published: June 22, 2012

Citation
Yazhuo Gong, David Zhang, Pengfei Shi, and Jingqi Yan, "Optimal wavelength band clustering for multispectral iris recognition," Appl. Opt. 51, 4275-4284 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-19-4275


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References

  1. C. K. Boyce, “Multispectral iris recognition analysis: techniques and evaluation,” Master’s thesis (West Virginia University, 2006), pp. 101–102.
  2. Z. Liu, J.-Q. Yan, D. Zhang, and Q.-L. Li, “Automated tongue segmentation in hyperspectral images for medicine,” Appl. Opt. 46, 8328–8334 (2007). [CrossRef]
  3. C. L. Wilkerson, N. A. Syed, M. R. Fisher, N. L. Robinson, I. H. L. Wallow, and D. M. Albert, “Melanocytes and iris color: light-microscopic findings,” Arch. Ophthalmol. 114, 437–442 (1996). [CrossRef]
  4. A. Ross, R. Pasula, and L. Hornak, “Exploring multispectral iris recognition beyond 900 nm,” in Proceedings of the IEEE Third International Conference on Biometrics: Theory, Applications, and Systems, 2009 (IEEE, 2009), pp. 1–8.
  5. J. Park and M. Kang, “Multispectral iris authentication system against counterfeit attack using gradient-based image fusion,” Opt. Eng. 46, 117003 (2007). [CrossRef]
  6. M. J. Burge and M. K. Monaco, “Multispectral iris fusion for enhancement, interoperability, and cross wavelength matching,” Proc. SPIE 7334, 73341D (2009). [CrossRef]
  7. L. Franssen, J. E. Coppens, and T. J. T. P. van den Berg, “Grading of iris color with an extended photographic reference set,” J. Opt. 1, 36–40 (2008). [CrossRef]
  8. W. Zuo, D. Zhang, and K. Wang, “Bidirectional PCA with assembled matrix distance metric for image recognition,” IEEE Trans. Syst. Man Cybern. B 36, 863–872(2006). [CrossRef]
  9. T. Cover and J. Thomas, Elements of Information Theory(Wiley, 1991).
  10. S. Ghosal, J. K. Ghosh, and R. V. Ramamoorthi, “Posterior consistency of Dirichlet mixtures in density estimation,” Ann. Stat. 27, 143–158 (1999). [CrossRef]
  11. S. Ghosal and A. W. van der Vaart, “Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities,” Ann. Stat. 29, 1233–1263 (2001).
  12. S. Ghosal and A. W. van der Vaart, “Posterior convergence rates of Dirichlet mixtures at smooth densities,” Ann. Stat. 35, 697–723 (2007). [CrossRef]
  13. S. T. Tokdar, “Posterior consistency of Dirichlet location-scale mixture of normals in density estimation and regression,” Sankhya 68, 90–110 (2006).
  14. D. H. Johnson and S. Sinanović, “Symmetrizing the Kullback–Leibler distance,” in Proceedings of IEEE Transactions on Information Theory (IEEE, 2001), pp. 1–10.
  15. K. Ozawa, “CLASSIC: a hierarchical clustering algorithm based on asymmetric similarities,” Pattern Recogn. 16, 201–211 (1983). [CrossRef]
  16. J. Daugman, “New methods in iris recognition,” IEEE Trans. Syst. Man Cybern. B 37, 1167–1175 (IEEE, 2007). [CrossRef]
  17. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 3rd ed. (Elsevier, 2006).
  18. B. Everitt, S. Landau, and M. Leese, Cluster Analysis, 4th ed. (Edward Arnold, 2001).
  19. L. Masek, “Recognition of human iris patterns for biometric identification,” M.S. thesis (University of Western Australia, 2003).
  20. H. Chang, Y. Yao, A. Koschan, B. Abidi, and M. Abidi, “Spectral range selection for face recognition under various illuminations,” in Proceedings of the 15th IEEE International Conference on Image Processing (IEEE, 2008), pp. 2756–2759.
  21. B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson, “Band selection for hyperspectral image classification using mutual information,” IEEE Geosci. Remote Sens. Lett. 3, 522–526 (2006). [CrossRef]
  22. H. Wang and E. Angelopoulou, “Sensor band selection for multispectral imaging via average normalized information,” J. Real-Time Image Proc. 1, 109–121 (2006). [CrossRef]
  23. A. A. Ross, K. Nadakumar, and A. K. Jain, Handbook of Multibiometrics (Springer, 2006).
  24. L. Comtet, Advanced Combinatorics: The Art of Finite and Infinite Expansions, rev. enl. ed. (Reidel, 1974), pp. 176–177.
  25. W. Mendenhall, R. J. Beaver, and B. M. Beaver, Probability and Statistics (Brooks/Cole, 2003).
  26. M. Vilaseca, R. Mercadal, J. Pujol, M. Arjona, M. de Lasarte, R. Huertas, M. Melgosa, and F. H. Imai, “Characterization of the human iris spectral reflectance with a multispectral imaging system,” Appl. Opt. 47, 5622–5630 (2008). [CrossRef]
  27. M. Vilaseca, J. Pujol, M. Arjona, and M. de Lasarte, “Multispectral system for reflectance reconstruction in the near infrared region,” Appl. Opt. 45, 4241–4253 (2006). [CrossRef]
  28. Z. Guo, L. Zhang, and D. Zhang, “Feature band selection for multispectral palmprint recognition,” in Proceedings of the 20th International Conference on Pattern Recognition (IEEE, 2010), pp. 1136–1139.
  29. Z. Guo, D. Zhang, L. Zhang, W. Zuo, and G. Lu, “Empirical study of light source selection for palmprint recognition,” Pattern Recogn. Lett. 32, 120–126 (2011). [CrossRef]
  30. H. Wang, and E. Angelopoulou, “Sensor band selection for multispectral imaging via average normalized information,” J. Real-Time Image Process. 1, 109–121 (2006). [CrossRef]
  31. J. C. Price, “Band selection procedure for multispectral scanners,” Appl. Opt. 33, 3281–3288 (1994). [CrossRef]
  32. M. Vatsa, R. Singh, and A. Noore, “Improving iris recognition performance using segmentation, quality enhancement, match score fusion and indexing,” Proc. IEEE Trans. Syst. Man Cybern. B 38, 1021–1035 (2008). [CrossRef]

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