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.
@article{arxiv.1906.09472,
title = {Iris Verification with Convolutional Neural Network and Unit-Circle Layer},
author = {Radim Spetlik and Ivan Razumenic},
journal= {arXiv preprint arXiv:1906.09472},
year = {2019}
}