Iris Classification Based on Its Quality

  • Aditya Nigam
  • Anvesh T.
  • Phalguni Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7995)

Abstract

This paper proposes an iris classification method based on the iris image quality. Quality of an iris image is modeled as a function of the attributes like focus, motion blur, occlusion, contrast and illumination, specular reflection and dilation. Values of these attributes are combined using a support vector machine (SVM) to provide the overall quality class of the image.

Keywords

SVM Image Quality Iris Biometrics Focus 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    The casia lamp iris database, http://iris.idealtest.org/
  2. 2.
    Badrinath, G.S., Nigam, A., Gupta, P.: An Efficient Finger-Knuckle-Print Based Recognition System Fusing Sift And Surf Matching Scores. In: InternationalConference on Information and Communications Security, pp. 374–387 (2011)Google Scholar
  3. 3.
    Badrinath, G.S., Tiwari, K., Gupta, P.: An Efficient Palmprint Based Recognition System Using 1d-Dct Features. In: Huang, D.-S., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2012. LNCS, vol. 7389, pp. 594–601. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Bendale, A., Nigam, A., Prakash, S., Gupta, P.: Iris segmentation using improved hough transform. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 408–415. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Dass, S.C., Jain, A.K.: Localized Iris Image Quality Using 2-D Wavelets. In: Proceedings of International Conference on Biometrics, pp. 373–381 (2006)Google Scholar
  6. 6.
    Daugman, J.: High Con_Dence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transansactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  7. 7.
    Daugman, J.: How Iris Recognition Works. In: Proceedings of International Conference on Image Processing, pp. 33–36 (2002)Google Scholar
  8. 8.
    Ferzli, R., Karam, L.J.: A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (Jnb). IEEE Transactions on Image Processing 18(4), 717–728 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kalka, N.D., Jinyu, Z., Schmid, N.A., Cukic, B.: Image Quality Assessment for Iris Biometric. In: Proceedings of SPIE. The International Society for Optical Engineering. Society of Photo-Optical Instrumentation Engineers (2006)Google Scholar
  10. 10.
    Kumar, J., Nigam, A., Prakash, S., Gupta, P.: An Efficient Pose Invariant Face Recognition System. In: International Conference on Soft Computing or Problem Soling, SocProS (2), pp. 145–152 (2011)Google Scholar
  11. 11.
    Masek, L.: Recognition of Human Iris Patterns for Biometric Identi_Cation. Technical report, University of Western Australia (2003)Google Scholar
  12. 12.
    Nigam, A., Gupta, P.: Finger Knuckleprint Based Recognition System Using Feature Tracking. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 125–132. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Nigam, A., Gupta, P.: A New Distance Measure for Face Recognition System. In: International Conference on Image and Graphics, ICIG, pp. 696–701 (2009)Google Scholar
  14. 14.
    Nigam, A., Gupta, P.: Comparing Human Faces Using Edge Weighted Dissimilarity Measure. In: International Conference on Control, Automation, Robotics and Vision, ICARCV, pp. 1831–1836 (2010)Google Scholar
  15. 15.
    Nigam, A., Gupta, P.: Iris Recognition Using Consistent Corner Optical Flow. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 358–369. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    Shafer, G.: A Mathematical Theory of Evidence (1976)Google Scholar
  17. 17.
    Singh, N., Nigam, A., Gupta, P., Gupta, P.: Four Slap Fingerprint Segmentation. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS, vol. 7390, pp. 664–671. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Tiwari, K., Arya, D.K., Gupta, P.: Palmprint Based Recognition System Using Local Structure Tensor and Force Field Transformation. In: Huang, D.-S., Gan, Y., Gupta, P., Gromiha, M.M. (eds.) ICIC 2011. LNCS, vol. 6839, pp. 602–607. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Wan, J., He, X., Shi, P.: An Iris Image Quality Assessment Method Based on Laplacian Of Gaussian Operation. In: Proceedings of Machine Vision Application (MVA) Conference. Citeseer, Tokyo (2007)Google Scholar
  20. 20.
    Wei, Z., Tan, T., Sun, Z., Cui, J.: Robust and Fast Assessment of Iris Image Quality. In: Proceedings of International Conference on Biometrics, pp. 464–471 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aditya Nigam
    • 1
  • Anvesh T.
    • 1
  • Phalguni Gupta
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KanpurKanpurIndia

Personalised recommendations