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A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications

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Title A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications
 
Creator Jyothi, P
Reddy, D Krishna
Kumar, P Naveen
 
Subject ANFIS
CNN
Data augmentation
Feature map
Genuine
Imposter
 
Description 151-157
The biometric authentication system is demanded to identify a particular person from the set of persons. Even though
many biometric authentication methods are available such as fingerprint, palm, face, and iris, the iris-based recognition
system is effective due to its simplified process. This article proposes an iris recognition system using a hybrid classification
approach for security applications. The proposed method includes three modules: preprocessing, augmentation, and
classifier. The preprocessing module converts the color iris images into grey scale images and also resizes the image into
256 × 256. The preprocessed iris images are now data augmented to construct the larger dataset. The data augmented images
are classified into either genuine or imposter images using a hybrid classification approach. The hybrid classification
approach functions in two modes as training and testing. In this article, the Convolutional Neural Networks (CNN) is
integrated with the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to enhance the recognition rate of the iris
recognition system. The performance analysis of the proposed approach is shown in terms of sensitivity, accuracy,
recognition rate, specificity, false-positive rate, and false-negative rate. The experimental results of the proposed iris
recognition system stated in this article significantly outweigh other design methods.
 
Date 2023-01-16T09:23:49Z
2023-01-16T09:23:49Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61194
https://doi.org/10.56042/jsir.v82i1.70253
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]