Correct Personal Iris Recognition at Long Distance by using Dougman’s Rubber Sheet Model
Swati D. Shirke1, C. Rajabhushnam2

1Ms. Swati Dattatraya Shirke, Ph.D Scholar, Department of CSE, Bharath Institute of Higher Education and Research. Bharath University, Chennai (Tamil Nadu), India.
2Dr. C. Rajabhushnam, Professor, Department of CSE, Bharath Institute of Higher Education and Research. Bharath University, Chennai (Tamil Nadu), India.
Manuscript received on 28 May 2019 | Revised Manuscript received on 15 June 2019 | Manuscript Published on 26 June 2019 | PP: 406-413 | Volume-8 Issue-1S5 June 2019 | Retrieval Number: A00720681S519/2019©BEIESP
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Abstract: Now a days, the personal identification of iris recognition acts as a powerful tool. This is because of iris has good stability, cannot change throughout the life and every person has different iris biometric characteristics. But in real instance it is very complicated to get a good quality of iris image. Therefore to make this iris recognition system more convenient, here this article presents an efficient iris recognition scheme which can capture iris image about 4 to 8 meter long distance accurately. While designing this system there are many key issued are occurred such as blur, image processing, human machine interference, iris image acquisition, etc. The different methods in this paper can resolve all of above problems. To develop this system this successfully different algorithm are used. The algorithm used are Hough Transform for detection of iris circle and edge, scaT T loop for feature extraction, Dougman’s rubber sheet model for normalization and segmentation, median filter and Trained neural network, etc. This iris recognition was tested on Casia V4 database. This system is developed on MATLAB for performing the Hough transform operations and for reading the iris images. The simulation results shows that this system successfully recognize the iris at a distance 4 to 8 meter.
Keywords: Matlab, Iris Recognition, Hough Transform, Image Normalization, Image Segmentation, Feature Extraction, Dougman’s Rubber Sheet Model, Trained Neural Network, etc.
Scope of the Article: Open Models and Architectures