Analysis of Iris Image Segmentation in a Color Space Model
Space Model S. B. Kulkarni1, Kirthishree C. Katti2, Arun A. Kumbi3, R. B. Kulkarni4, Vinita K. Tapaskar5
1Dr. S. B. Kulkarni, Assistant Professor, Dept. of CSE, SDMCET, Dharwad, India.
2Kirthishree C Katti, Dept. of CSE, SDMCET, Dharwad, India.
3Arun A Kumbi, Dept. CSE, SKSVMACET, Laxmeshwar, India.
4R. B. Kulkarni, Dept. of CSE, Shiradi Sai Engineering College, Bangalore, Karnataka, (A.P). India.
5Vinuta K. Tapaskar, Lecturer, Department of Computer Science & Applications at The Oxford College of Science, Bangalore, Karnataka, (A.P). India.
Manuscript received on May 24, 2014. | Revised Manuscript received on June 12, 2014. | Manuscript published on June 30, 2014. | PP: 50-56 | Volume-3, Issue-5, June 2014. | Retrieval Number: E3092063514/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper presents the iris localization using circular Hough transform. Circular Hough transform localized the inner and outer boundaries of the iris. The software of the application is based on detecting the circles surrounding the exterior iris pattern from a set of images in different spaces. The iris segmentation system is based on the combination of the canny edge detection method, adaptive histogram Equalization method, circular Hough Transform and Euclidean Distance formula methods. The main part of Iris recognition is the segmentation of iris part of the eye. The performance of the segmentation is analyzed using UBIRIS, IITD, PALACKY, MMU database with adaptive parameters.
Keywords: Segmentation, Canny Edge Detection, Adaptive Histogram Equalization, Circular Hough Transform and Euclidean Distance Formula.