Iris Verification with Convolutional Neural Network and Unit-Circle Layer
This work addresses iris verification for biometric security, presenting an incremental improvement with specific gains over existing methods.
The authors tackled iris verification by proposing a convolutional neural network with a novel Unit-Circle Layer, achieving state-of-the-art results with a 10% improvement over the best method on the CASIA.v4 dataset and up to 15% performance gain on unseen data.
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.