Texture Descriptor Extraction for Iris Classification using Fuzzy Logic Local Binary Pattern

Feature extraction in the iris classification field is an essential stage that is applied to produce a discriminative descriptor for each subject. Uncertainty due to contrast and other noises may degrade the exactness of descriptors between subject. In achieving to tackle the problem, fuzzy logic lo...

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Bibliographic Details
Published in2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) pp. 29 - 32
Main Author Ali, Ahmad N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2020
Subjects
Feature extraction
Fuzzy logic
Image segmentation
Iris
iris classification
Iris recognition
Kernel
local binary pattern
Support vector machines
vector support machines
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Summary:Feature extraction in the iris classification field is an essential stage that is applied to produce a discriminative descriptor for each subject. Uncertainty due to contrast and other noises may degrade the exactness of descriptors between subject. In achieving to tackle the problem, fuzzy logic local binary pattern concept that uses a specific rule to decide the uncertainty intensity values between central and neighboring pixels has been applied for feature extraction. An experimental setup has been performed to evaluate the concept using two freely available databases which are CASIA V3 and IITD. A promising result with more than 98% of the classification score success rate has achieved by using Support Vector Machines classifier.
DOI:10.1109/ICCSCE50387.2020.9204949