Non-cooperative Iris Segmentation: A Two-stage Approach

: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. At first, modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. Then, the global threshold-based segmentation is employed for pupil region segmentation. The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques.