# Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks

## TL;DR

This paper introduces three new deep learning-based iris segmentation methods, demonstrating improved segmentation accuracy over traditional methods across diverse datasets, and discusses their integration with Gabor-wavelet-based iris recognition.

## Contribution

The paper presents novel open-source deep learning segmentation techniques and a methodology for using irregular masks in iris recognition, validated on diverse datasets for better generalization.

## Key findings

- Deep learning segmentation outperforms conventional methods in segmentation accuracy.
- Deep learning methods achieve better generalization across diverse iris datasets.
- Gabor-based iris matching performance is comparable with Daugman's segmentation when using deep learning masks.

## Abstract

This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition. To train and validate the methods, we used a wide spectrum of iris images acquired by different teams and different sensors and offered publicly, including data taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS, Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied training data should increase the generalization capabilities of the proposed segmentation techniques. In database-disjoint training and testing, we show that deep learning-based segmentation outperforms the conventional (OSIRIS) segmentation in terms of Intersection over Union calculated between the obtained results and manually annotated ground-truth. Interestingly, the Gabor-based iris matching is not always better when deep learning-based segmentation is used, and is on par with the method employing Daugman's based segmentation.