REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION

Meryem Erbilek, Önsen Toygar

Abstract

The difficulty in the process of human identification by iris recognition is that the iris images captured may have occlusions by the eyelids and eyelashes. In that case, recognition of occluded iris patterns becomes hard and the corresponding person may not be correctly recognized. In order to reduce the effect of eyelid or eyelash occlusion on the recognition of human beings by their iris patterns, we propose a simple and efficient method for iris recognition using specific regions on the iris images without using the traditional preprocessing approach before applying the feature extraction method to recognize the irises. First of all, these regions are individually experimented and then the outputs of each region are combined using a multiple classifier combination method with the feature extraction method Principal Component Analysis (PCA). The experiments on the iris images, with and without occlusions, demonstrate that the proposed approach achieves better recognition rates compared to the recognition rates of the holistic approaches.

References

  1. CASIA-IrisV3, http://www.cbsr.ia.ac.cn/IrisDatabase.htm Chu, C.T., Chen, C.H., 2005. High Performance Iris Recognition Based on LDA and LPCC. In ICTAI'05, Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, Hong Kong.
  2. Cui, J., Huang, J., Tan, T., Sun, Z., 2004. An iris image synthesis method based on PCA and super-resolution. In ICPR'04, 17th International Conference on Pattern Recognition, Cambridge, UK.
  3. Daugman, J., 2006. Probing The Uniqueness and Randomness of IrisCodes: Results from 200 Billion Iris Pair Comparisons. Proceedings of The IEEE, Vol.94, No.11, pp.1927-1935.
  4. Ho, T., Hull, J., Srihari, S., 1994. Decision combination in multiple classifier systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, pp. 66-75.
  5. Kirby, M. and Sirovich, L., 1990. Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces. In IEEE Trans. Pattern Analysis and Machine Intelligence. Vol.12, pp. 103-107.
  6. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J., 1998. On Combining Classifiers, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.20, No.3, pp.226-239.
  7. Ma, L., Tan T., Wang Y., Zhang D., 2004. Efficient iris recognition by characterizing key local variations. In IEEE Transactions on Image Processing. Vol.13, no.6, pp.739-750.
  8. Turk, M. and Pentland, A., 1991. Eigenfaces for Recognition. In Journal of Cognitive Neuroscience. Vol.3, pp.71-86.
  9. Toygar, Ö. and Acan, A., 2004. Multiple Classifier Implementation of a Divide-and-Conquer Approach Using Appearance-Based Statistical Methods for Face Recognition. In Pattern Recognition Letters, Vol.25, pp. 1421-1430.
Download


Paper Citation


in Harvard Style

Erbilek M. and Toygar Ö. (2008). REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 543-546. DOI: 10.5220/0001084305430546


in Bibtex Style

@conference{visapp08,
author={Meryem Erbilek and Önsen Toygar},
title={REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={543-546},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001084305430546},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION
SN - 978-989-8111-21-0
AU - Erbilek M.
AU - Toygar Ö.
PY - 2008
SP - 543
EP - 546
DO - 10.5220/0001084305430546