Efficient iris segmentation using Grow-Cut algorithm for remotely acquired iris images

Abstract

This paper presents a computationally efficient iris segmentation approach for segmenting iris images acquired from at-a-distance and under less constrained imaging conditions. The proposed iris segmentation approach is developed based on the cellular automata which evolves using the Grow-Cut algorithm. The major advantage of the developed approach is its computational simplicity as compared to the prior iris segmentation approaches developed for the visible illumination iris segmentation images. The experimental results obtained from the three publicly available databases, i.e. UBIRIS.v2, FRGC and CASIA.v4-distance have respectively achieved average improvement of 34.8%, 31.5% and 31.4% in the average segmentation error, as compared to the recently proposed competing/best approaches. The experimental results presented in this paper clearly demonstrate the superiority of the developed iris segmentation approach, i.e., significant reduction in computational complexity while providing comparable segmentation performance, for the distantly acquired iris images.