Iris recognition is regarded as the most reliable and accurate biometric identification system available. Iris recognition system captures an image of an individual's eye, the iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation. Segmentation is used to locate the correct iris region in an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region.In our book we are comparing two segmentation methods namely, Daughman's algorithm and Hough Transform. Iris images are selected from the CASIA Database, then the iris and pupil boundary are detected from rest of the eye image, removing the noises.The segmented iris region was normalized to eliminate dimensional inconsistencies between iris regions by using Daugman's Rubber Sheet Model.A comparative analysis is made of the two methods to find out the better method.