Advertisement

Performance Analysis of Iris Recognition System

  • Ruqaiya Khanam
  • Zohreen Haseen
  • Nighat Rahman
  • Jugendra Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 847)

Abstract

A biometric system offers automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Although iris identification system is based on pattern recognition technique but due to poor iris boundary detection and high computational time in previous work, we used neural network and discriminant machine learning technique to obtained high accuracy. In this work, we implement neural network and discriminant analysis of machine learning method for iris recognition in iris images to implement in day-to-day life, using MATLAB 2016a. The emphasis will be only on the software for performing recognition and not hardware for capturing an eye image. The proposed method gives better recognition rate than SVM technique with less computational complexity. Neural network and discriminant methods are used for matching and finding recognition accuracy. Thus, the accuracy obtained from neural network is 94.44%, whereas from discriminant analysis the accuracy obtained is 99.99%.

Keywords

Iris recognition Neural network Machine learning 

References

  1. 1.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.: Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560, 1 Mar 1994Google Scholar
  3. 3.
    Wolff, E.: Anatomy of the Eye and Orbit, 7th edn. H. K. Lewis & Co. LTDGoogle Scholar
  4. 4.
    Chinese Academy of Sciences—Institute of Automation: Database of 756 grayscale eye images. http://www.sinobiometrics.com Version 1.0 (2003)
  5. 5.
    Oppenheim, A., Lim, J.: The importance of phase in signals. Proc. IEEE 69, 529–541 (1999)CrossRefGoogle Scholar
  6. 6.
    Zhu, Y., Tan, T., Wang, Y.: Biometric personal identification based on iris pattern. In: ICPR2000: The 15th International Conference on Pattern Recognition, pp. 805–808, Barcelona, Spain (2002)Google Scholar
  7. 7.
    CASIA iris image database. http://www.sinobiometrics.com
  8. 8.
    Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improvement of feature vector and classifier. ETRI J. 2, 61–70 (2001)CrossRefGoogle Scholar
  9. 9.
    Daugman, J.: How iris recognition works. In: Proceedings of 2002 International Conference on Image Processing, vol. 1 (2002)Google Scholar
  10. 10.
    Kaur, N., Juneja, M.: A review on iris recognition. In: Proceedings of 2014 RAECS, UIET Panjab University, Chandigarh, 6–8 Mar 2014Google Scholar
  11. 11.
    Khan, M.T., Arora, D.: Feature extraction through iris images using 1-D Gabor filter on different iris datasets. IEEE (2013). ISBN 978-1-4799-0192-0/13Google Scholar
  12. 12.
    Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8) (2010)CrossRefGoogle Scholar
  13. 13.
    Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. (2008). IEEEGoogle Scholar
  14. 14.
    Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensic Secur. 4(4) (2009)CrossRefGoogle Scholar
  15. 15.
    He, Z., Tan, T., Sun, Z., Qiu, X.: Towards accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal. Mach. Intell. (2008)Google Scholar
  16. 16.
    Ashwini, M.B., Imran, M., Alsaade, F.: Evaluation of iris recognition system on multiple feature extraction algorithms and its combinations. Int. J. Comput. Appl. Technol. Res. 4(8), 592–598 (2015). ISSN 2319–8656Google Scholar
  17. 17.
  18. 18.
    Wildes, R., Asmuth, J., Green, G., Hsu, S., Kolczynski, R., Matey, J., McBride, S.: A system for automated iris recognition. In: Proceedings IEEE Workshop on Applications of Computer Vision, pp. 121–128, Sarasota, FL (1994)Google Scholar
  19. 19.
    Kong, W., Zhang, D.: Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong (2001)Google Scholar
  20. 20.
    Tisse, C., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: International Conference on Vision Interface, Canada (2002)Google Scholar
  21. 21.
    Ma, L., Wang, Y., Tan, T.: Iris Recognition Using Circular Symmetric Filters. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing (2002)Google Scholar
  22. 22.
    Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4) (1998)CrossRefGoogle Scholar
  23. 23.
    Lee, T.: Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10) (1996)Google Scholar
  24. 24.
    Jain, A.K., Ross, A.: Introduction to biometrics. In: Jain, A.K., Flynn, P., Ross, A. (eds.) Handbook of Biometrics, pp. 1–22. Springer (2008). ISBN 978-0-387-71040-2Google Scholar
  25. 25.
    Sreekala, P., Jose, V., Joseph, J., Joseph, S.: The human iris structure and its application in security system of car. In: IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA) (2012)Google Scholar
  26. 26.
    Shah, N., Shrinath, P.: Iris recognition system—a review. Int. J. Comput. Inform. Technol. 3(2) (2014)Google Scholar
  27. 27.
    Wibowo, E.P., Maulana, W.S.: Real-time iris recognition system using a proposed method. In: International Conference on Signal Processing Systems, pp. 98–102, 15–17 May 2009Google Scholar
  28. 28.
    Zhiping, Z., Maomao, H., Ziwen, S.: An iris recognition method based on 2DWPCA and neural network. In: Chinese Control and Decision Conference (CCDC ’09), pp. 2357–2360, June 2009Google Scholar
  29. 29.
    Sciencedirect.com: A new texture analysis approach for iris recognition. Online Available: http://en.wikipedia.org/wiki/Iris_recognition (2015)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ruqaiya Khanam
    • 1
  • Zohreen Haseen
    • 1
  • Nighat Rahman
    • 1
  • Jugendra Singh
    • 1
  1. 1.Galgotias UniversityGreater NoidaIndia

Personalised recommendations