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Advancing Cross-Spectral Iris Recognition Research Using Bi-Spectral Imaging

  • N. Pattabhi Ramaiah
  • Ajay Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 390)

Abstract

Iris images are increasingly employed in national ID programs and large-scale iris databases have been developed. Conventional iris images for the biometrics databases are acquired from close distances under near infrared wavelengths. However the surveillance data is often acquired under visible wavelengths. Therefore, applications like watch-list identification and surveillance at-a-distance require accurate iris matching capability for images acquired under different wavelengths. In this context, simultaneously acquired visible iris images should be matched with the iris images acquired under near infrared illumination to ascertain cross-spectral iris recognition accuracy. This paper describes the need for such cross-spectral iris recognition capability and proposes the development of bi-spectral iris image database to advance the much needed research in this area.

Notes

Acknowledgments

This work is supported by research grant from Hong Kong Research Grant Council grant no. PolyU/152068/14E.

References

  1. 1.
    Global Biometrics Market (2014–2020): Market Forecast By Technologies, Applications, End Use, Regions and Countries. http://www.researchandmarkets.com/research/6lngtl/global_biometrics/
  2. 2.
    Daugman, John G.: Pattern analysis and machine intelligence. IEEE Trans. High Confid. Vis. Recognit. Pers. Test Stat. Indep. 15(11), 1148–1161 (1993)Google Scholar
  3. 3.
    Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. Elsevier 110(2), 281–307 (2008)CrossRefGoogle Scholar
  4. 4.
    Proença, H.: Non-cooperative iris recognition: issues and trends. In: Proceedings of the EUSIPCO, pp. 1–5 (2011)Google Scholar
  5. 5.
    Imai, F.H.: Preliminary experiment for spectral reflectance estimation of human iris using a digital camera, Munsell Color Science Laboratory Technical Report (2002)Google Scholar
  6. 6.
    Proença, H., Alexandre, L.A.: UBIRIS: a noisy iris image database. In: Springer Proceedings of International Conference on Image Analysis and Processing, pp. 970–977 (2005)Google Scholar
  7. 7.
    Proença, H., Filipe, S., Santos, R., Oliveira, J.: Alexandre Luis: The ubiris. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010)Google Scholar
  8. 8.
    Dobes, M., Machala, L.: UPOL iris image database. http://www.phoenix.inf.upol.cz/iris/ (2004)
  9. 9.
    Tajbakhsh, N., Araabi, B.N., Soltanianzadeh, H.: Feature fusion as a practical solution toward noncooperative iris recognition. In: IEEE Proceedings of 11th International Conference on Information Fusion, pp. 1–7 (2008)Google Scholar
  10. 10.
    Tan, C.-W., Kumar, A.: Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Process. 21(9), 4068–4079 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ross, A., Pasula, R., Hornak, L.: Exploring multispectral iris recognition beyond 900 nm. In: IEEE Proceedings of 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–8 (2009)Google Scholar
  12. 12.
    Sharma, A., Verma, S., Vatsa, M., Singh, R.: On cross spectral periocular recognition. In: IEEE Proceedings of 21st International Conference on Image Processing, pp. 5007–5011 (2014)Google Scholar
  13. 13.
    Zuo, J., Nicolo, F., Schmid, N.A.: Cross spectral iris matching based on predictive image mapping. In: IEEE Proceedings of 4th International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5 (2010)Google Scholar
  14. 14.
    Pillai, Jaishanker K., Puertas, Maria, Chellappa, Rama: Cross-sensor iris recognition through Kernal learning. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 73–85 (2014)CrossRefGoogle Scholar
  15. 15.
    Xiao, L., Sun, Z., He, R., Tan, T.: Coupled feature selection for cross-sensor iris recognition. In: IEEE Proceedings of 6th International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2013)Google Scholar
  16. 16.
    Kulis, B., Sustik, M., Dhillon, I.: Learning low-rank kernel matrices. In: ACM Proceedings of the 23rd International Conference on Machine Learning, pp. 505–512 (2006)Google Scholar
  17. 17.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ACM Proceedings of the 24th International Conference on Machine Learning, pp. 209–216 (2007)Google Scholar
  18. 18.
    JAI, Multi-spectral imaging, http://www.jai.com/en/products/
  19. 19.
    Intersil, Eye safety for proximity sensing using infrared light-emitting diodes, http://www.intersil.com/content/dam/Intersil/documents/an17/an1737.pdf
  20. 20.
    Masek, Libor and Kovesi, Peter, Matlab source code for a biometric identification system based on iris patterns, The School of Computer Science and Software Engineering, The University of Western Australia, 2(4), (2003)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHung Hom, Hong KongChina

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