HYBRID TECHNIQUE OF IRIS RECOGNITION AND IRIS TEMPLATE MATCHING USING DAUGMANS AND GABOR WAVELET MODELS
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Keywords
Daugman's rubber sheet model, Segmentation, Iris recognition, Normalization.
Abstract
- Iris recognition is the process of recognizing a person by analyzing the random pattern of the iris. Iris scan biometrics employs the unique characteristics and features of the human iris in order to verify the identity of an individual. The iris is the area of the eye where it is pigmented or color circle, usually brown or blue. Iris recognition systems use small, high-quality cameras to capture a black and white high-resolution photograph of the iris. This process takes only one two seconds are provide the details of the iris that are mapped, recorded and stored for future mapping.
The main objective of this algorithm is to detect and enhancing pupil detection for efficient and fast with less mathematical burden on system. To implement efficiently even though upper portion of the eye is densely covered by eyelashes. To improve overall performance of the system and achieve accuracy with minimized execution time compare than the existing methods. Iris recognition processing generally consists of the following steps: (i) Image acquisition (ii) Iris segmentation and (iii) Normalization. In this algorithm implemented, segmentation was achieved using the Hough transform for localizing the iris and pupil regions. The segmented iris region was normalized to a rectangular block with fixed polar dimensions using Daugman's rubber sheet model.
References
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J. Jeya Bargan
Research Scholar, Registration Number: 20211252272005, Department of Information Technology, Sri Ram Nallamani Yadava College of Arts and Science, Tenkasi, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India.
Dr. D. Kiruba Jothi
Research Supervisor, Sri Ram Nallamani Yadava College of Arts and Science, Tenkasi