Comparing Haar-Hilbert and Log-Gabor Based Iris Encoders on Bath Iris Image Database

TL;DR

This paper introduces a new iris encoding method using Haar Wavelet and Hilbert Transform, compares it with Log-Gabor encoders on Bath Iris Database, and achieves competitive recognition accuracy.

Contribution

Proposes a novel iris encoder combining Haar Wavelet Transform and Hilbert Transform, and provides comparative analysis with Log-Gabor encoders on a standard database.

Findings

Achieved an Equal Error Rate comparable to the lowest reported for Bath Iris Database.

Demonstrated the effectiveness of Haar-Hilbert encoders in iris recognition.

Released new MATLAB tools for iris image processing.

Abstract

This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log- Gabor iris encoder. Twelve tests involving single/multienrollment and conducted on Bath Iris Image Database are presented here. One of these tests achieves an Equal Error Rate comparable to the lowest value reported so far for this database. New Matlab tools for iris image processing are also released together with this paper: a second version of the Circular Fuzzy Iris Segmentator (CFIS2), a fast Log-Gabor encoder and two Haar-Hilbert based encoders.