Deep Verification Learning

Fieke Hillerström, Raymond Veldhuis, Luuk Spreeuwers

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    Abstract

    Deep learning for biometrics has increasingly gained attention over the last years.
    The expansion of computational power and the increasing dataset sizes, increased
    verification performances. However, large datasets are not available for every ap-
    plication. We introduce Deep Verification Learning, to reduce network complex-
    ity and train on smaller datasets. Deep Verification Learning takes two images
    to be verified at the input of a network, and trains directly towards a verification
    score. We applied Deep Verification Learning on the face verification task, also
    it could be extended to other biometric modalities.
    Original languageEnglish
    Title of host publicationProceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux
    Subtitle of host publicationMay 11-12, 2017, Delft University of Technology, Delft, the Netherlands
    EditorsRichard Heusden, Jos H. Weber
    Place of PublicationDelft
    PublisherDelft University of Technology
    Pages97-104
    ISBN (Print)978-94-6186-811-4
    Publication statusPublished - 2017
    Event38th WIC Symposium on Information Theory in the Benelux 2017 - Delft, Netherlands
    Duration: 11 May 201712 May 2017
    Conference number: 38

    Conference

    Conference38th WIC Symposium on Information Theory in the Benelux 2017
    CountryNetherlands
    CityDelft
    Period11/05/1712/05/17

    Keywords

    • SCS-Safety
    • FISWG
    • Eyebrows
    • Human verification performance

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  • Cite this

    Hillerström, F., Veldhuis, R., & Spreeuwers, L. (2017). Deep Verification Learning. In R. Heusden, & J. H. Weber (Eds.), Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands (pp. 97-104). Delft: Delft University of Technology.