DeepIris: Iris Recognition Using A Deep Learning Approach

TL;DR

DeepIris introduces a residual CNN-based end-to-end framework for iris recognition, achieving promising results with limited training data and providing visualization of key iris regions, advancing biometric security methods.

Contribution

The paper presents a novel deep learning framework that jointly learns features and performs iris recognition, with a visualization technique for important iris regions.

Findings

Achieved improved recognition accuracy over previous methods.

Effective with limited training images per class.

Provides visualization of critical iris areas.

Abstract

Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control. Different features and algorithms have been proposed for iris recognition in the past. In this paper, we propose an end-to-end deep learning framework for iris recognition based on residual convolutional neural network (CNN), which can jointly learn the feature representation and perform recognition. We train our model on a well-known iris recognition dataset using only a few training images from each class, and show promising results and improvements over previous approaches. We also present a visualization technique which is able to detect the important areas in iris images which can mostly impact the recognition results. We believe this framework can be widely used for other biometrics recognition tasks, helping to have a more scalable and accurate systems.