Iris Segmentation Approach Based on Adaptive Threshold Value and Circular Hough Transform

Researchers have proposed several approaches to provide processing methodologies for iris images captured in unconstrained mediums to leverage the level of accuracy for iris recognition systems. Segmentation is the most critical stage which considered a challenging area to researchers. In this paper, we propose an iris segmentation approach to handle the problem of low contrast iris images, in which the iris boundary is undetected. It uses the pupil boundary to define a search space for automatically finding an appropriate threshold value to extract the iris region, and then uses the thresholded image to create binary edge map with strong iris edge. Circular Hough Transform (CHT) is adopted to localize pupil/iris boundaries, and Rubber Sheet Model (RSM) of lower half of iris is used in normalization stage to eliminate upper eyelashes and eyelid. Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique is adopted to overcome the low contrast problem of iris image. Finally, a region of interest without the impact of lower eyelashes and eyelid is selected to obtain noise free iris template. The proposed approach is tested on CASIA Iris Image Dataset Version 2.0.