Human Identification and Verification using Iris Recognition by Calculating Hamming Distance
Ashish kumar Dewangan1, Majid Ahmad Siddhiqui2
1Ashish Kumar Dewangan, M.Tech. Scholar (Digital Electronics), RCET, Bhilai, Chhattisgarh, India. Also working as an Assistant Professor in Department of electronics and telecommunication, CSIT, Durg, Chhattisgarh, India.
2Majid Ahmad Siddhiqui, M.Tech, Reader in Department of electronics and telecommunication, RCET, Bhilai, Chhattisgarh, India.
Manuscript received on April 11, 2012. | Revised Manuscript received on April 14, 2012. | Manuscript published on May 05, 2012. | PP: 334-338 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0646042212/2012©BEIESP
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© The Authors. Published By: 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: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favorable conditions, and there have been no independent trials of the technology. The work presented in this paper involved developing an ‘open-source’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system one databases of digitized grayscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.
Keywords: Automatic segmentation, Biometric identification, Iris recognition, Pattern recognition.