Hough Transform Method for Iris Recognition-A Biometric Approach
Prateek Verma, Maheedhar Dubey, Somak Basu, Praveen Kumar Verma · 2012
Iris recognition is most accurate and reliable biometric identification system available in the current scenario. Iris recognition system captures an image of an individual's eye, the iris in the image is then meant for segmentation and normalized for feature extraction process. The performance of iris recognition systems highly depends on the segmentation process. Segmentation is used for the localization of 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 paper we are using Hough Transform segmentation method for Iris Recognition. 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 minimize the dimensional inconsistencies between iris regions by using Daugman's Rubber Sheet Model. Then for the features of the iris were encoded by convolving the normalized iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris.