Shallow CNNs for the Reliable Detection of Facial Marks

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    Abstract

    Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.

    Original languageEnglish
    Title of host publication2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018
    EditorsArslan Bromme, Andreas Uhl, Christoph Busch, Christian Rathgeb, Antitza Dantcheva
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    ISBN (Electronic)978-3-88579-676-3
    ISBN (Print)978-1-5386-6007-2
    DOIs
    Publication statusPublished - 10 Oct 2018
    Event17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018 - Darmstadt, Germany
    Duration: 26 Sep 201828 Sep 2018
    Conference number: 17

    Publication series

    NameInternational Conference of the Biometrics Special Interest Group (BIOSIG)
    PublisherIEEE
    Volume2018
    ISSN (Electronic)1617-5468

    Conference

    Conference17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018
    Abbreviated titleBIOSIG 2018
    CountryGermany
    CityDarmstadt
    Period26/09/1828/09/18

    Keywords

    • CNN
    • Facial marks
    • Forensics
    • Image processing

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