Iris Verification with Convolutional Neural Network and Unit-Circle Layer

  • Radim ŠpetlíkEmail author
  • Ivan Razumenić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)


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.



Radim Špetlík was supported by the OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 and by CTU student grant SGS17/ 185/OHK3/3T/13.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Czech Technical UniversityPragueCzech Republic
  2. 2.Microsoft Development Center SerbiaBelgradeSerbia

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