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Iris Localization Using Mixture of Probability Distributions in the Segmentation Process

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Book cover Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 552))

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Abstract

Iris segmentation plays an important role in an accurate iris recognition system. In this paper, we contribute to a more accurate iris segmentation process, hence, a more precise identification tool. We introduce a novel iris segmentation approach based on mixture of probability distributions modeling and an extended Expectation Maximization based on the (EM) algorithm. A segmentation approach is presented by exploring different techniques. Our analysis takes care of segmenting the eye image using the Markovian case and the independent case. In each case, the approach uses different mixture of probability distributions. While providing comparable results, our update algorithm gives better segmentation results based on our learning system. The accuracy of our algorithm is proved by Kulback-Leibler divergence and mean squared error computations. The statistical significance of our results are evaluated using Anova Test. The proposed method was applied on the CASIA testing database by Chinese Academy of Sciences Institute of Automation-Iris-Twins. The results showed, for both cases, the accuracy of the proposed algorithm verses the classical mixture normal distribution.

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Authors and Affiliations

  1. Laboratory CES-Lab National School of Engineering, Sfax, Tunisia

    Fatma Mallouli & Mohamed Abid

Authors
  1. Fatma Mallouli
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  2. Mohamed Abid
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Corresponding author

Correspondence to Fatma Mallouli .

Editor information

Editors and Affiliations

  1. (MIR Labs), Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

  4. Technopolis Rabat-Shore Rocade, International University of Rabat, Sala el Jadida, Morocco

    Ghita Mezzour

  5. Inst Applied Dept of Electronics, Taffala, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Mallouli, F., Abid, M. (2017). Iris Localization Using Mixture of Probability Distributions in the Segmentation Process. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_49

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  • DOI: https://doi.org/10.1007/978-3-319-52941-7_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52940-0

  • Online ISBN: 978-3-319-52941-7

  • eBook Packages: EngineeringEngineering (R0)

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