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Pupil Segmentation Using Stirling’s Interpolation Based Fractional Differential Mask

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Computational Intelligence, Cyber Security and Computational Models. Recent Trends in Computational Models, Intelligent and Secure Systems (ICC3 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1631))

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Abstract

Pupil localization is one of the significant pre-processing steps in iris recognition system, which plays a vital role in human authentication system. This paper proposes a novel approach for computerized pupil segmentation. A new fractional differential mask based on Stirling’s interpolation, has been employed to snip the pupil efficiently from the iris image. The pupil is segmented based on the dynamic threshold. The new mask and the dynamic threshold are the key factors in segmenting the pupil region. The proposed algorithm is implemented using MATLAB and tested on two public iris databases such as CASIA V1.0 and MMU2. Experimental results clearly indicate that the proposed method is comparably accurate in segmenting pupil with existing methods irrespective of its shape; the proposed method is also capable of handling low contrast images, specular reflections or images occluded by eyelids and eyelashes.

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References

  1. Abdullah, M.A.M., Dlay, S.S., Woo, W.L., Chambers, J.A.: Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans. Syst. Man Cybern. 99, 1–14 (2016)

    Google Scholar 

  2. Basit, A., Javed, M.: Localization of iris in grayscale image using intensity gradient. Opt. Lasers Eng. 45, 1107–1114 (2007)

    Article  Google Scholar 

  3. Li, B., **e, W.: Adaptive fractional differential approach and its application to medical image enhancement. Comput. Electr. Eng. 45, 324–335 (2015)

    Article  Google Scholar 

  4. Boyina, S.R., Anu, H., Moneca, K., Mahalakshmi, G.M., Priyadharshini, R.: Pupil recognition using wavelet and fuzzy linear discriminant analysis for authentication. Int. J. Adv. Eng. Technol. (2012)

    Google Scholar 

  5. CASIA iris image database (Ver 1.0). http://biometrics.idealtest.org/

  6. Gao, C., Zhou, J., Lang, F., Liu, C.: A new fractional differential mask for image enhancement. J. Convergence Inf. Technol. 8(4), (2013)

    Google Scholar 

  7. Lacoviello, D., Lucchetti, M.: Parametric characterization of the form of the human pupil from blurred noisy images. Comput. Methods Programs Biomed. 77, 39–48 (2005)

    Google Scholar 

  8. Daugman, J.G.: High confidence visual recognition of person by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993)

    Article  Google Scholar 

  9. Chen, D., Bai, J., Qu, Z.: Research on pupil center location based on improved Hough transform and edge gradient algorithm. In: National Conference on Information Technology and Computer Science, pp. 47–51 (2012)

    Google Scholar 

  10. Hu, F., Si, S., Wong, H.S., Fu, B., Si, M.X., H. Luo: An adaptive approach for texture enhancement based on a fractional differential operator with non-integer step and order. Neurocomputing 158, 295–306 (2015)

    Google Scholar 

  11. ImageJ software. https://imagej.nih.gov/ij/index.html

  12. Kiruthiga, A.R., Arumuganathan, R.: Fractional differential mask based retinal image enhancement – a new approach. Transylvanian Rev. XXIV(10), 1647–1659 (2016)

    Google Scholar 

  13. Kiruthiga, A.R., Arumuganathan, R.: Smoothening of iris images and pupil segmentation using fractional derivative and wavelet transform. In: 4th International Conference on Signal Processing, Communications and Networking (ICSCN), pp. 1–6. IEEE (2017). https://doi.org/10.1109/ICSCN.2017.8085713

  14. Yu, L., Cao, Q., Zhao, A.: Principal patterns of fractional-order differential gradients for face recognition. J. Electr. Imaging 24(1), 013021 (2015)

    Article  Google Scholar 

  15. Mehrabian, H., Hashemi-Tari, P.: Pupil boundary detection for Iris recognition using Graph cuts. In: Proceedings of Image and Vision Computing, pp. 77–82. New Zealand (2007)

    Google Scholar 

  16. MMU 2 Iris Image Database. http://pesona.mmu.edu.my/~ccteo/

  17. Ibrahim, M.T., Khan, T.M., Khan, S.A., Khan, M.A., Guan, L.: Iris localization using local histogram and other image statistics. Opt. Lasers Eng. 50, 645–654 (2012)

    Article  Google Scholar 

  18. Sahoo, N., Padhy, G., Bhoi, N., Rautaray, P.: Automatic localization of pupil using Histogram thresholding and region mask filter. Soft Comput. Tech. Vis. Sci. SCI 395, 55–62 (2012)

    Article  Google Scholar 

  19. He, N., Wang, J.-B., Zhang, L.-L., Lu, K.: An improved fractional-order differentiation model for image denoising. Sig. Process. 112, 180–188 (2015)

    Article  Google Scholar 

  20. Oldham, K.B., Spanier: The Fractional Calculus: Integrations and Differentiations of Arbitrary Order. Academic, New York (1974)

    Google Scholar 

  21. Samko, S.G., Kilbas, A.A., Marichev, O.I.: Fractional integrals and derivatives: theory and applications. Yverdon, Swizterland. Gordon and Breach (1993)

    Google Scholar 

  22. Khan, T.M., Kong, Y., Khan, M.A.U.: Hardware implementation of fast pupil segmentation using region properties. In: 12th International Conference on Quality Control by Artificial Vision. Proceedings of SPIE 9534 (2015)

    Google Scholar 

  23. Khan, T.M., Khan, M.A., Malik, S.A., Khan, S.A., Bashir, T., Dar, A.H.: Automatic localization of pupil using eccentricity and iris using gradient based method. Opt. Lasers Eng. 49, 177–187 (2011)

    Article  Google Scholar 

  24. Teo, C.C., Neo, H.F., Michael, G.K.O., Tee, C., Sim, K.S.: A robust iris segmentation with fuzzy supports. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) Proceedings of ICONIP 2010. LNCS, vol. 6443, pp. 532–539. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17537-4_65

  25. Roselin, V., Waghmare, L.M.: Pupil detection and feature extraction algorithm for Iris recognition. Adv. Model. Optim. 15(2) (2013)

    Google Scholar 

  26. Wildes, R.P.: Iris Recognition: an emerging biometric technology. Proc. IEEE 85, 1348–1363 (1997)

    Article  Google Scholar 

  27. Pu, Y.-F., Zhou, J.-L., Yuan, X.: Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement. IEEE Trans. Image Process. 19(2), 491–511 (2010)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors wish to thank the Chinese Academy of Science-Institute of Automation (CASIA) for providing CASIA Version 1.0 iris database and Multimedia University for providing MMU2 iris database.

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Authors and Affiliations

  1. Researcher, Coimbatore, Tamil Nadu, India

    A. R. Kiruthiga

  2. Department of Mathematics, PSG College of Technology, Coimbatore, India

    R. Arumuganathan

Authors
  1. A. R. Kiruthiga
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  2. R. Arumuganathan
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Corresponding author

Correspondence to A. R. Kiruthiga .

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Editors and Affiliations

  1. Coimbatore, India

    Indhumathi Raman

  2. Coimbatore, India

    Poonthalir Ganesan

  3. Coimbatore, India

    Venkatasamy Sureshkumar

  4. Coimbatore, India

    Latha Ranganathan

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Kiruthiga, A.R., Arumuganathan, R. (2022). Pupil Segmentation Using Stirling’s Interpolation Based Fractional Differential Mask. In: Raman, I., Ganesan, P., Sureshkumar, V., Ranganathan, L. (eds) Computational Intelligence, Cyber Security and Computational Models. Recent Trends in Computational Models, Intelligent and Secure Systems. ICC3 2021. Communications in Computer and Information Science, vol 1631. Springer, Cham. https://doi.org/10.1007/978-3-031-15556-7_11

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  • DOI: https://doi.org/10.1007/978-3-031-15556-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15555-0

  • Online ISBN: 978-3-031-15556-7

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