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Iris Recognition in Image Domain: Quality-Metric Based Comparators

  • Heinz Hofbauer
  • Christian Rathgeb
  • Andreas Uhl
  • Peter Wild
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

Traditional iris recognition is based on computing efficiently coded representations of discriminative features of the human iris and employing Hamming Distance (HD) as fast and simple metric for biometric comparison in feature space. However, the International Organization for Standardization (ISO) specifies iris biometric data to be recorded and stored in (raw) image form (ISO/IEC FDIS 19794-6), rather than in extracted templates (e.g. iris-codes) achieving more interoperability as well as vendor neutrality. In this paper we propose the application of quality-metric based comparators operating directly on iris textures, i.e. without transformation into feature space. For this task, the Structural Similarity Index measure (SSIM), Local Edge Gradients metric (LEG), Natural Image Contour Evaluation (NICE), Edge Similarity Score (ESS) and Peak Signal to Noise ratio (PSNR) is evaluated. Obtained results on the CASIA-v3 iris database confirm the applicability of this type of iris comparison technique.

Keywords

Iris reconition biometric comparators image quality-metrics image domain 

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References

  1. 1.
    Alonso-Fernandez, F., Tome-Gonzalez, P., Ruiz-Albacete, V., Ortega-Garcia, J.: Iris recognition based on sift features. In: Int’l Conf. on Biometrics, Ident. and Sec (BIdS), pp. 1–8 (2009)Google Scholar
  2. 2.
    Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: A survey. Comp. Vis. Image Underst. 110(2), 281 (2008)CrossRefGoogle Scholar
  3. 3.
    Daugman, J.: How iris recognition works. IEEE Trans. Circ. and Syst. for Video Techn. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  4. 4.
    Kekre, H.B., Thepade, S.D., Jain, J., Agrawal, N.: Iris recognition using texture features extracted from haarlet pyramid. Int’l J. of Comp. App. 11(12), 1–5 (2010); Found. Comp. Sc. Google Scholar
  5. 5.
    Hofbauer, H., Uhl, A.: An Effective and Efficient Visual Quality Index based on Local Edge Gradients. In: IEEE 3rd Europ. Workshop on Visual Inf. Proc., p. 6 (2011)Google Scholar
  6. 6.
    Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: The best bits in an iris code. IEEE Trans. on Pattern Anal. and Mach. Intell. 31(6), 964–973 (2009)CrossRefGoogle Scholar
  7. 7.
    Kekre, H.B., Thepade, S.D., Jain, J., Agrawal, N.: Iris recognition using texture features extracted from walshlet pyramid. In: Prof. Int’l Conf. & Workshop on Emerging Trends in Techn (ICWET), pp. 76–81. ACM (2011)Google Scholar
  8. 8.
    Ko, J.-G., Gil, Y.-H., Yoo, J.-H., Chung, K.-I.: A novel and efficient feature extraction method for iris recognition. ETRI Journal 29(3), 399–401 (2007)CrossRefGoogle Scholar
  9. 9.
    Krichen, E., Garcia-Salicetti, S., Dorizzi, B.: A new phase-correlation-based iris matching for degraded images. IEEE Trans. on Systems, Man, and Cyb., Part B 39(4), 924–934 (2009)CrossRefGoogle Scholar
  10. 10.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. on Image Processing 13(6), 739–750 (2004)CrossRefGoogle Scholar
  11. 11.
    Mao, Y., Wu, M.: Security evaluation for communication-friendly encryption of multimedia. In: IEEE Int’l Conf. on Image Proc, ICIP (2004)Google Scholar
  12. 12.
    Matey, J., Naroditsky, O., Hanna, K., Kolczynski, R., LoIacono, D., Mangru, S., Tinker, M., Zappia, T., Zhao, W.Y.: Iris on the move: Acquisition of images for iris recognition in less constrained environments. Proc. IEEE 94, 1936–1947 (2006)CrossRefGoogle Scholar
  13. 13.
    Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: An efficient iris recognition algorithm using phase-based image matching. In: IEEE Int’l Conf. on Image Proc. (ICIP), pp. 49–52 (2005)Google Scholar
  14. 14.
    Rathgeb, C., Uhl, A., Wild, P.: Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity. In: Proc. Int’l Conf. on Biometrics: Theory, App., and Syst (BTAS), pp. 1–6 (2010)Google Scholar
  15. 15.
    Rathgeb, C., Uhl, A., Wild, P.: Iris-biometric comparators: Minimizing trade-offs costs between computational performance and recognition accuracy. In: Proc. Int’l Conf. on Imaging for Crime Det. and Prev (ICDP), pp. 1–7 (2011)Google Scholar
  16. 16.
    Rouse, D., Hemami, S.S.: Natural image utility assessment using image contours. In: IEEE Int’l Conf. on Image Proc (ICIP), pp. 2217–2220 (2009)Google Scholar
  17. 17.
    Rouse, D., Hemami, S.S.: The role of edge information to estimate the perceived utility of natural images. In: Western New York Image Proc. Workshop (WNYIP), p. 4 (2009)Google Scholar
  18. 18.
    Tomeo-Reyes, I., Liu-Jimenez, J., Rubio-Polo, I., Fernandez-Saavedra, B.: Quality metrics influence on iris recognition systems performance. In: IEEE Int’l Carnahan Conf. on Security Technology (ICCST), pp. 1–7 (2011)Google Scholar
  19. 19.
    Uhl, A., Wild, P.: Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation. In: Proc. Int’l Conf. on Biometrics, ICB (to appear, 2012)Google Scholar
  20. 20.
    Vatsa, M., Singh, R., Noore, A., Ross, A.: On the dynamic selection of biometric fusion algorithms. IEEE Trans. on Inf. Forensics and Sec. 10(3), 470–479 (2010)CrossRefGoogle Scholar
  21. 21.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Proc. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  22. 22.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P.S. (ed.) Graphics Gems IV, pp. 474–485. Morgan Kaufmann (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Heinz Hofbauer
    • 1
  • Christian Rathgeb
    • 1
  • Andreas Uhl
    • 1
  • Peter Wild
    • 1
  1. 1.Multimedia Signal Processing and Security Lab, Department of Computer SciencesUniversity of SalzburgAustria

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