Iris Recognition Based on LBP and Combined LVQ Classifier

TL;DR

This paper introduces a hybrid iris recognition system combining Local Binary Pattern features with a combined LVQ classifier, achieving high accuracy across multiple datasets.

Contribution

It proposes a novel hybrid model integrating LBP and a combined LVQ classifier for improved iris recognition performance.

Findings

Achieved 99.87% recognition accuracy on various datasets.

Effective localization and segmentation using Canny and Hough Transform.

Robust performance with different iris datasets.

Abstract

Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.