Skip to main content
  • Research Article
  • Open access
  • Published:

Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis

Abstract

Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition.

References

  1. Jain AK, Bolle R, Pankanti S (Eds): Biometrics: Personal Identification in Networked Society. Kluwer Academic, Dordrecht, The Netherlands; 1999.

    Google Scholar 

  2. Zhang D: Automated Biometrics: Technologies and Systems. Kluwer Academic, Boston, Mass, USA; 2000.

    Book  Google Scholar 

  3. Wildes RP: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 1997,85(9):1348-1363. 10.1109/5.628669

    Article  Google Scholar 

  4. Wolff E: Anatomy of the eye and orbit. 7th edition. H. K. Lewis, London, UK; 1976.

    Google Scholar 

  5. Bertillon A: La Couleur de l'Iris. Rev. of Science 1885,36(3):65-73.

    Google Scholar 

  6. Daugman JG: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993,15(11):1148-1161. 10.1109/34.244676

    Article  Google Scholar 

  7. Daugman JG: Demodulation by complex-valued wavelets for stochastic pattern recognition. International Journal of Wavelets, Multiresolution, and Information Processing 2003,1(1):1-17. 10.1142/S0219691303000025

    Article  Google Scholar 

  8. Ma L, Wang Y, Tan T: Iris recognition using circular symmetric filters. Proceedings of the 16th International Conference on Pattern Recognition, August 2002, Quebec City, Quebec, Canada 2: 414–417.

    Article  Google Scholar 

  9. Ma L, Tan T, Wang Y, Zhang D: Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(12):1519-1533. 10.1109/TPAMI.2003.1251145

    Article  Google Scholar 

  10. Ma L, Wang Y, Tan T: Personal iris recognition based on multichannel Gabor filtering. Proceedings of the 5th Asian Conference on Computer Vision (ACCV '02), January 2002, Melbourne, Australia

    Google Scholar 

  11. Wildes RP, Asmuth JC, Green GL, et al.: A machine-vision system for iris recognition. Machine Vision and Applications 1996,9(1):1-8. 10.1007/BF01246633

    Article  Google Scholar 

  12. Boles W, Boashash B: A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing 1998,46(4):1085-1088. 10.1109/78.668558

    Article  Google Scholar 

  13. Tisse C, Martin L, Torres L, Robert M: Person identification technique using human iris recognition. Proceedings of the 15th International Conference on Vision Interface (VI '02), May 2002, Calgary, Canada 294–299.

    Google Scholar 

  14. Lim S, Lee K, Byeon O, Kim T: Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal 2001,23(2):61-70. 10.4218/etrij.01.0101.0203

    Article  Google Scholar 

  15. Nam KW, Yoon KL, Bark JS, Yang WS: A feature extraction method for binary iris code construction. Proceedings of the 2nd International Conference on Information Technology for Application (ICITA '04), January 2004, Harbin, China

    Google Scholar 

  16. Ma L, Tan T, Wang Y, Zhang D: Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing 2004,13(6):739-750. 10.1109/TIP.2004.827237

    Article  Google Scholar 

  17. Sanchez-Reillo R, Sanchez-Avila C: Iris recognition with low template size. Proceedings of the 3rd International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA '01), June 2001, Halmstad, Sweden 324–329.

    Chapter  Google Scholar 

  18. Sanchez-Avila C, Sanchez-Reillo R: Iris-based biometric recognition using dyadic wavelet transform. IEEE Aerospace and Electronic Systems Magazine 2002,17(10):3-6. 10.1109/MAES.2002.1044509

    Article  Google Scholar 

  19. Jablonski P, Szewczyk R, Kulesza Z, Napieralski A, Moreno M, Cabestany J: Automatic people identification on the basis of iris pattern image processing and preliminary analysis. Proceedings of the 23rd International Conference on Microelectronics (MIEL '02), May 2002, Nis, Yugoslavia 2: 687–690.

    Google Scholar 

  20. Szewczyk R, Jablonski P, Kulesza Z, Napieralski A, Cabestany J, Moreno M: Automatic people identification on the basis of iris pattern extraction features and classification. Proceedings of the 23rd International Conference on Microelectronics (MIEL '02), May 2002, Nis, Yugoslavia 2: 691–694.

    Google Scholar 

  21. Park C-H, Lee J-J, Smith MJT, Park K-H: Iris-based personal authentication using a normalized directional energy feature. Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA '03), June 2003, Guildford, UK 224–232.

    Chapter  Google Scholar 

  22. Vijaya Kumar BVK, Xie C, Thornton J: Iris verification using correlation filters. Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA '03), June 2003, Guildford, UK 697–705.

    Chapter  Google Scholar 

  23. Bae K, Noh S-I, Kim J: Iris feature extraction using independent component analysis. Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA '03), June 2003, Guildford, UK 838–844.

    Chapter  Google Scholar 

  24. Gu H-Y, Zhuang Y-T, Pan Y-H: An iris recognition method based on multi-orientation features and non-symmetrical SVM. Journal of Zhejiang University: Science 2005,6 A(5):428-432. A0505

    Article  Google Scholar 

  25. Poursaberi A, Araabi BN: Binary representation of iris patterns for individual identification: sensitivity analysis. Proceedings of the 8th International Conference on Pattern Recognition and Information Processing (PRIP '05), May 2005, Minsk, Belarus

    Google Scholar 

  26. Poursaberi A, Araabi BN: A half-eye wavelet based method for iris recognition. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA '05), September 2005, Wroclaw, Poland

    Google Scholar 

  27. Camus T, Salganicoff M, Thomas A, Hanna K: Method and apparatus for removal of bright or dark spots by the fusion of multiple images. United States patent no. 6088470, 1998

    Google Scholar 

  28. McHugh J, Lee J, Kuhla C: Handheld iris imaging apparatus and method. United States patent no. 6289103, 1998

    Google Scholar 

  29. Poursaberi A, Araabi BN: A fast morphological algorithm for iris detection in eye images. Proceedings of the 6th Iranian Conference on Intelligent Systems, December 2004, Kerman, Iran

    Google Scholar 

  30. Daugman J: Statistical richness of visual phase information: update on recognizing persons by iris patterns. International Journal of Computer Vision 2001,45(1):25-38. 10.1023/A:1012365806338

    Article  Google Scholar 

  31. Poursaberi A, Araabi BN: An iris recognition system based on Daubechies's wavelet phase. Proceedings of the 6th Iranian Conference on Intelligent Systems, December 2004, Kerman, Iran

    Google Scholar 

  32. https://doi.org/www.sinobiometrics.com/

  33. Mansfield T, Kelly G, Chandler D, Kane J: Biometric product testing final report. issue 1.0, National Physical Laboratory of UK, 2001

    Google Scholar 

  34. Mansfield A, Wayman J: Best practice standards for testing and reporting on biometric device performance. National Physical Laboratory of UK, 2002

    Google Scholar 

  35. Ma L: Personal identification based on iris recognition, Ph.D. dissertation.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Poursaberi.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Poursaberi, A., Araabi, B. Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis. EURASIP J. Adv. Signal Process. 2007, 036751 (2006). https://doi.org/10.1155/2007/36751

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/36751

Keywords