# Iris Verification with Convolutional Neural Network and Unit-Circle Layer

## TL;DR

This paper introduces a new CNN with a Unit-Circle Layer for iris verification, achieving state-of-the-art accuracy and significant performance improvements over existing methods on multiple datasets.

## Contribution

The paper presents a novel CNN architecture with a unique Unit-Circle Layer that replaces Gabor-filtering, enhancing iris verification accuracy.

## Key findings

- Achieved 10% higher accuracy than the previous best on CASIA.v4.
- The Unit-Circle Layer improves performance by up to 15% on unseen data.
- Validated on three public datasets with state-of-the-art results.

## Abstract

We propose a novel convolutional neural network to verify a~match between two normalized images of the human iris. The network is trained end-to-end and validated on three publicly available datasets yielding state-of-the-art results against four baseline methods. The network performs better by a 10% margin to the state-of-the-art method on the CASIA.v4 dataset. In the network, we use a novel Unit-Circle Layer layer which replaces the Gabor-filtering step in a common iris-verification pipeline. We show that the layer improves the performance of the model up to 15% on previously-unseen data.