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Iris recognition using deep learning

Juš Lozej (2018) Iris recognition using deep learning. MSc thesis.

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    Despite the large increase of deep learning solutions in recent years, no deep learning iris pipelines have yet been developed. Inspired by conventional iris recognition pipelines, we present our general deep architecture for iris recognition. The presented deep iris pipeline is an end-to-end convolutional neural network consisting of two high-level blocks: segmentation and recognition. The segmentation part is tasked with the generation of binary mask, which corresponds with the surface of the iris. These masks are multiplied with the original iris image and then fed to the recognition part. The recognition part extracts meaningful iris features, which are then used for matching. Our model achieved high results on both testing datasets. On Casia-Iris-Thousand it achieved a Rank-1 accuracy of 95.12% and on SBVPI an accuracy of 92.33%. We also implemented a cross-database model, trained on samples from both dataset, which achieved an accuracy of 88.53%. Our deep pipeline outperformed a conventional iris pipeline in speed and accuracy. As far as we are aware, our pipeline is the first implementation of an end-to-end deep neural network, which is able to segment and recognize the iris image. As opposed to current deep models, which perform recognition on a pre-normalized iris image, our method uses original iris images.

    Item Type: Thesis (MSc thesis)
    Keywords: Iris recognition, deep learning, convolutional neural networks, biometrics
    Number of Pages: 79
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Peter Peer294Mentor
    izr. prof. dr. Vitomir ŠtrucComentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1538047939)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4306
    Date Deposited: 05 Nov 2018 15:13
    Last Modified: 23 Nov 2018 13:35
    URI: http://eprints.fri.uni-lj.si/id/eprint/4306

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