An Efficient Approach for Pupil Detection in Iris Images

Iris-based biometric system is gaining its importance in several applications. However, existing methods of detecting boundary between pupil and iris, which is the first task in any iris-based biometric identification methods are computationally expensive. Further, existing methods are not able to detect pupil boundary accurately and hence leading to errors in identification process. In this paper, we address these two problems and propose a technique to detect pupil boundary efficiently and accurately. We propose scaling and power transform followed by edge detection and circle finding. Scaling reduces the search space significantly and power transform is helpful for image thresholding. Experiments on CASIA iris database reveal that with the proposed approach, we able to detect pupil almost 100% accurately.