Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours

Segmentation is an important step in iris recognition framework because the accuracy of the iris recognition system is affected by the segmentation of the iris. The image acquisition introduces noise artefacts such as specular reflections, eyelids/eyelashes occlusions and overlapping intensities, which makes the segmentation process difficult. An efficient method has been proposed for the segmentation of iris images that deal with non-circular iris boundaries and other noise artefacts mentioned above. The proposed method uses the Otsu multilevel thresholding based on improved particle swarm optimisation technique as a pre-segmentation step. Pre-segmentation step delimits the iris region from the other parts of an eye image. The geodesic active contours incorporated with a novel stopping function is then used to segment non-circular iris boundaries. The recognition accuracy of the proposed method is verified using the standard databases, CASIA v3 Interval and UBIRISv1. Obtained results have been compared with existing methods and have an encouraging performance.