Optimized biometric system based iris-signature for human identification

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Optimized biometric system based iris-signature for human identification
 
Creator Hamd, Muthana Hachim
 
Subject Iris recognition; Fourier descriptors; Iris-Signature; Neural network; Optimization techniques
 
Description This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2019-10-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/407
10.26555/ijain.v5i3.407
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 273-284
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/407/ijain_v5i3_p273-284
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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