Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

Iris segmentation is foremost part of iris recognition system. There are four steps in iris recognition: segmentation, normalization, encoding and matching. Here, iris segmentation has been implemented using Hough Transform and Integro- Differential Operator techniques. The performance of iris recognition system depends on segmentation and normalization technique. Iris recognition systems capture an image from individual eye. Then the image captured is segmented and normalized for encoding process. The matching technique, Hamming Distance, is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage. These processes produce values of average circle pupil, average circle iris, error rate and edge points. The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR). Hough Transform algorithm, provide better performance, at the expense of higher computational complexity. It is used to evolve a contour that can fit to a non-circular iris boundary. However, edge information is required to control the evolution and stopping the contour. The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information. The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator