@Article{2019.100000069,
AUTHOR = {Fatma Mallouli},
TITLE = {Robust EM Algorithm for Iris Segmentation Based on Mixture of Gaussian  Distribution},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {243--248},
URL = {http://www.techscience.com/iasc/v25n2/39656},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000069}
}