Abstract
This paper discusses an analysis of human iris patterns for recognition of biometric system which consists of a 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 is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. To encode the unique pattern of the iris into a bit-wise biometric template, 1D Log-Gabor filter is used.Finally to match two iris templates hamming distance is used as matching metric. The system performance is analyzed on 312 iris images taken from standard CASIA Iris Interval database version 4. To establish the verification accuracy of iris representation and matching approach, each iris image in the database is matched with all the other iris images in the database and genuine and imposter distribution is found .The performance of the system is implemented by evaluating the Decidability Index (DI), False match rate (FMR), False Non-match rate (FNMR), Genuine Accept Rate (GAR) and Equal error rate (EER).
Highlights
A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual
Metric Used For Performance Evaluation The performance of the implemented system is evaluated on the following biometric metric parameters, 3.1 Decidability Index The key objective of an iris recognition system is to be able to achieve a distinct separation of intra-class and inter-class Hamming distance distributions
CASIA-Iris-Interval is well-suited for studying the detailed texture features of iris images
Summary
A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. The implemented system uses a segmentation algorithm based on the circular Hough transform for detecting the iris and pupil boundaries [8].This involves first employing Canny edge detection to generate an edge map. 2.2 Iris Normalization Once the iris region is successfully segmented from an eye image, the stage is to transform the iris region so that it has fixed dimensions in order to allow comparisons.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
