The Performance Enhancement Systems of Human Iris Pattern and Recognition Method through Digital Authentication Application

International Journal of Machine Learning and Networked Collaborative Engineering

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Field Value
 
Title The Performance Enhancement Systems of Human Iris Pattern and Recognition Method through Digital Authentication Application
 
Creator N, Krishnaveni
Sharma, Yogesh Kumar
Nagaprasad, Sriramula
 
Subject Digital Voting, Iris Recognition, Segmentation, Feature Extraction, Accuracy
 
Description Human iris and recognition patterns have been recognized as the best biometric marking ever found, owing to the uniqueness of iris and the textured iris patterns tend to remain natural, unchangeable and recognizable through existence. Mathematical analyses of the special stable patterns formed within the iris include Iris detection methods and a comparative analysis is carried out utilizing an established database. In this document, a clean electoral system is created to build a fraud-free ID list of electors. To find the Iris and Eyes, the algorithm of canny edge detection is used, Dougman's normalization procedure is used, object filters are added and finally the corresponding process is conducted for the Euclidian set. Biometric authentication confirms our identification by being a simple and increasingly secure method. We implement a weighted, majority voting process for all biometric authentication systems utilizing a bit wise contrast between inscription and biometric models to resolve this problem and to enable Iris identification in less than ideal images. We also observed that the approach outdoes the current majority and efficient bit sorting strategies through a set of tests with the database CASIA iris.  Our approach is an easy and efficient way to boost the accuracy of established iris detection systems.
 
Publisher SR Informatics, New Delhi, India
 
Date 2020-08-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/137
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 4 No. 01 (2020): Volume No 04 Issue No 01; 40-52
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/137/78
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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