Robust Iris Segmentation under Unconstrained Settings

João C. Monteiro, Hélder P. Oliveira, Ana F. Sequeira, Jaime S. Cardoso

2013

Abstract

The rising challenges in the field of iris recognition, concerning the development of accurate recognition algorithms using images acquired under an unconstrained set of conditions, is leading to the a renewed interest in the area. Although several works already report excellent recognition rates, these values are obtained by acquiring images in very controlled environments. The use of such systems in daily security activities, such as airport security and bank account management, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focused on mutual context information from iris centre and iris limbic contour to perform robust and accurate iris segmentation in noisy images. A random subset of the UBIRIS.v2 database was tested with a promising E1 classification rate of 0.0109.

References

  1. Almeida, P. (2010). A knowledge-based approach to the iris segmentation problem. Image and Vision Computing, 28(2):238-245.
  2. Chen, R., Lin, X., and Ding, T. (2011). Iris segmentation for non-cooperative recognition systems. Image Processing, 5(5):448 -456.
  3. Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N., and Andrian, J. (2010). A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image and Vision Computing, 28(2):261 - 269.
  4. Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148 -1161.
  5. Daugman, J. (2006). Probing the uniqueness and randomness of iriscodes: Results from 200 billion iris pair comparisons. Proceedings of the IEEE, 94(11):1927- 1935.
  6. Daugman, J. (2007). New methods in iris recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,, 37(5):1167 -1175.
  7. He, Z., Tan, T., Sun, Z., and Qiu, X. (2009). Toward accurate and fast iris segmentation for iris biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9):1670 -1684.
  8. Jain, A., Hong, L., and Pankanti, S. (2000). Biometric identification. Communications of the ACM, 43(2):90-98.
  9. Kobatake, H. and Hashimoto, S. (1999). Convergence index filter for vector fields. IEEE Transactions on Image Processing, 8(8):1029-1038.
  10. Ma, L., Tan, T., Wang, Y., and Zhang, D. (2004). Local intensity variation analysis for iris recognition. Pattern Recognition, 37(6):1287 - 1298.
  11. Masek, L. (2003). Recognition of Human Iris Patterns for Biometric Identification. Towards Non-cooperative Biometric Iris Recognition. PhD thesis.
  12. Nabti, M. and Bouridane, A. (2008). An effective and fast iris recognition system based on a combined multiscale feature extraction technique. Pattern Recognition, 43(3):868 - 879.
  13. Oliveira, H., Cardoso, J., Magalhaes, A., and Cardoso, M. (2012). Simultaneous detection of prominent points on breast cancer conservative treatment images. In Proceedings of the 19th IEEE International Conference on Image Processing, pages 2841-2844.
  14. Pawar, M., Lokande, S., and Bapat, V. (2012). Iris segmentation using geodesic active contour for improved texture extraction in recognition. International Journal of Computer Applications, 47(16):448-456.
  15. Proenc¸a, H., Filipe, S., Santos, R., Oliveira, J., and Alexandre, L. A. (2010). The ubiris.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8):1529 -1535.
  16. Radman, A., Jumari, K., and Zainal, N. (2012). Iris segmentation in visible wavelength environment. Procedia Engineering, 41:743-748.
  17. Ross, A. (2010). Iris recognition: The path forward. Computer, 43(2):30-35.
  18. Sanchez-Avila, C., Sanchez-Reillo, R., and de MartinRoche, D. (2002). Iris-based biometric recognition using dyadic wavelet transform. Aerospace and Electronic Systems Magazine, IEEE, 17(10):3 - 6.
  19. Sankowski, W., Grabowski, K., Napieralska, M., Zubert, M., and Napieralski, A. (2010). Reliable algorithm for iris segmentation in eye image. Image and Vision Computing, 28(2):231-237.
  20. Shah, S. and Ross, A. (2009). Iris segmentation using geodesic active contours. IEEE Transactions on Information Forensics and Security,, 4(4):824 -836.
  21. Tan, C. and Kumar, A. (2012). Unified framework for automated iris segmentation using distantly acquired face images. IEEE Transactions on Image Processing, 21(9):4068-4079.
  22. Tan, T., He, Z., and Sun, Z. (2010). Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image and Vision Computing, 28(2):223 - 230.
  23. Vatsa, M., Singh, R., and Noore, A. (2008). Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,, 38(4):1021 -1035.
  24. Wildes, R. (1997). Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9):1348 - 1363.
  25. Zuo, J. and Schmid, N. (2010). On a methodology for robust segmentation of nonideal iris images. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,, 40(3):703 -718.
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Paper Citation


in Harvard Style

Monteiro J., Oliveira H., Sequeira A. and Cardoso J. (2013). Robust Iris Segmentation under Unconstrained Settings . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 180-190. DOI: 10.5220/0004281701800190


in Bibtex Style

@conference{visapp13,
author={João C. Monteiro and Hélder P. Oliveira and Ana F. Sequeira and Jaime S. Cardoso},
title={Robust Iris Segmentation under Unconstrained Settings},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={180-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004281701800190},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Robust Iris Segmentation under Unconstrained Settings
SN - 978-989-8565-47-1
AU - Monteiro J.
AU - Oliveira H.
AU - Sequeira A.
AU - Cardoso J.
PY - 2013
SP - 180
EP - 190
DO - 10.5220/0004281701800190