Pupil and Iris Detection Algorithm for Near-Infrared Capture Devices

  • Adam Szczepański
  • Krzysztof Misztal
  • Khalid Saeed
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8838)

Abstract

In this paper a simple and robust solution for the pupil and iris detection is presented. The procedure is based on simple operations, such as erosion, dilation, binarization, flood filling and Sobel filter and, with proper implementation, is effective. The novelty of the approach is the use of distances of black points from nearest white points to estimate and then adjust the position of the center and the radius of the pupil which is also used for iris detection. The obtained results are promising, the pupil is extracted properly and all the information necessary for human identification and verification can be extracted from the found parts of the iris. The paper, being both review and research, contains also a state of the art in the described topic.

Keywords

iris detection pupil detection gradient analysis linear analysis 

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References

  1. 1.
    Masek, L., et al.: Recognition of human iris patterns for biometric identification. M. Thesis, The University of Western Australia 3 (2003)Google Scholar
  2. 2.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)CrossRefGoogle Scholar
  3. 3.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85, 1348–1363 (1997)CrossRefGoogle Scholar
  4. 4.
    Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing 46, 1185–1188 (1998)CrossRefGoogle Scholar
  5. 5.
    Huang, Y.P., Luo, S.W., Chen, E.Y.: An efficient iris recognition system. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 450–454. IEEE (2002)Google Scholar
  6. 6.
    Zhu, Y., Tan, T., Wang, Y.: Biometric personal identification based on iris patterns. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 801–804. IEEE (2000)Google Scholar
  7. 7.
    Misztal, K., Saeed, E., Tabor, J., Saeed, K.: Iris Pattern Recognition with a New Mathematical Model to Its Rotation Detection, pp. 43–65. Springer, New York (2012)Google Scholar
  8. 8.
    Jillela, R., Ross, A.A.: Methods for iris segmentation. In: Handbook of Iris Recognition, pp. 239–279. Springer (2013)Google Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital image processing (2002)Google Scholar
  10. 10.
    Illingworth, J., Kittler, J.: A survey of the hough transform. Computer Vision, Graphics, and Image Processing 44, 87–116 (1988)CrossRefGoogle Scholar
  11. 11.
    Wildes, R.P., Asmuth, J.C., Green, G.L., Hsu, S.C., Kolczynski, R.J., Matey, J., McBride, S.E.: A system for automated iris recognition. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 121–128. IEEE (1994)Google Scholar
  12. 12.
    Xie, Y., Ji, Q.: A new efficient ellipse detection method. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 957–960. IEEE (2002)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Adam Szczepański
    • 1
  • Krzysztof Misztal
    • 1
  • Khalid Saeed
    • 1
    • 2
  1. 1.Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland
  2. 2.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland

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