Open Access
ARTICLE
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,
Intelligent Automation & Soft Computing 2019, 25(2), 243-248. https://doi.org/10.31209/2019.100000069
Abstract
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.
Keywords
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. https://doi.org/10.31209/2019.100000069