Implementation of Unsupervised Statistical Methods for Low-Quality Iris Segmentation
Meriem Yahiaoui, Emmanuel Monfrini, Bernadette Dorizzi · 2014
In this paper, we explore the use of advanced statistical models for unsupervised segmentation of challenging eye images. A previous work has shown the superiority of Triplet Markov Field (TMF) over HMF for segmenting challenging eye region but TMF implementation is computationally very expensive. To enable faster processing while preserving performance, we investigate in this paper Hidden Markov Chain (HMC) and Pair wise Markov Chain (PMC). We developed novel adequate image scanning procedures and initialization steps for implementing these models and extensive experiments on challenging images of the ICE2005 database show that the use of HMC with the snail scan and Histogram Initialization enhances the quality of segmentation comparing to OSIRIS-V4 based on contour approach or TMF model.