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Robust EM Algorithm for Iris Segmentation Based on Mixture of Gaussian Distribution

Fatma Mallouli

Imam Abdulrahman Bin Faisal University, Deanship of Preparatory Year and Supporting Studies, Dammam, Kingdom of Saudi Arabia

* Corresponding Author: Fatma Mallouli, email

Intelligent Automation & Soft Computing 2019, 25(2), 243-248.


Density estimation via Gaussian mixture modelling has been successfully applied to image segmentation. In this paper, we have learned distributions mixture model to the pixel of an iris image as training data. We introduce the proposed algorithm by adapting the Expectation-Maximization (EM) algorithm. To further improve the accuracy for iris segmentation, we consider the EM algorithm in Markovian and non Markovian cases. Simulated data proves the accuracy of our algorithm. The proposed method is tested on a subset of the CASIA database by Chinese Academy of Sciences Institute of Automation-IrisTwins. The obtained results have shown a significant improvement of our approach compared to the standard version of EM algorithm and the classical segmentation method.


Cite This Article

F. Mallouli, "Robust em algorithm for iris segmentation based on mixture of gaussian distribution," Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 243–248, 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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