Iris segmentation exploring color spaces

This paper describes a new method for iris segmentation using HSI and RGB color spaces. The outer and inner boundaries of the iris are extracted using the k-means unsupervised clusterization method. For the outer boundary detection the best results is obtained using as input variables of the clusterization method the red and green components of the RGB space. The final outer boundary is detected through the application of a modified version of the Hough Transform. For the inner boundary detection the best result is obtained using as input variables of the clusterization method the hue component of the HSI space. The method was tested with images of section 1 and 2 of the UBIRIS image database. For the section 1 the overall percent accuracy achieved was 97.6%. For section 2 the overall percent accuracy achieved was 93.7%. Some examples of the iris segmentation are provided in the results. A simple method for iris extraction associated with successful results obtained with noise iris images is the main contribution of this paper.