Iris Verification with Convolutional Neural Network and Unit-Circle Layer

Radim Spetlík, Ivan Razumenic

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 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.