Iris Biometric System using a hybrid approach

TL;DR

This paper presents a hybrid iris biometric system that combines boundary detection, noise reduction, image enhancement, and Haar Wavelet-based feature extraction, validated on CASIA database images for improved identification accuracy.

Contribution

It introduces a hybrid approach integrating multiple techniques for iris recognition, including circular Hough Transform and Haar Wavelets, enhancing accuracy and robustness.

Findings

Effective boundary localization using circular Hough Transform

Successful feature encoding with Haar Wavelets

Validated system performance on CASIA database

Abstract

Iris Recognition Systems are ocular- based biometric devices used primarily for security reasons. The complexity and the randomness of the Iris, amongst various other factors, ensure that this biometric system is inarguably an exact and reliable method of identification. The algorithm is responsible for automatic localization and segmentation of boundaries using circular Hough Transform, noise reductions, image enhancement and feature extraction across numerous distinct images present in the database. This paper delves into the various kinds of techniques required to approximate the pupillary and limbic boundaries of the enrolled iris image, captured using a suitable image acquisition device and perform feature extraction on the normalized iris image with the help of Haar Wavelets to encode the input data into a binary string format. These techniques were validated using images from the CASIA database, and various other procedures were also tried and tested.