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- W4286470043 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." @default.
- W4286470043 created "2022-07-22" @default.
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- W4286470043 date "2019-06-22" @default.
- W4286470043 modified "2023-10-18" @default.
- W4286470043 title "Iris Verification with Convolutional Neural Network and Unit-Circle Layer" @default.
- W4286470043 doi "https://doi.org/10.48550/arxiv.1906.09472" @default.
- W4286470043 hasPublicationYear "2019" @default.
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