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    • University of Missouri-Kansas City
    • School of Graduate Studies (UMKC)
    • Theses and Dissertations (UMKC)
    • Dissertations (UMKC)
    • 2020 Dissertations (UMKC)
    • 2020 UMKC Dissertations - Freely Available Online
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    Ocular motion classification for mobile device presentation attack detection

    Lowe, Jesse
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    [PDF] Ocular motion classification for mobile device presentation attack detection (16.81Mb)
    Date
    2020
    Metadata
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    Abstract
    As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, ocular motion based presentation attack detection (PAD) methods provide a potential deterrent. This dissertation presents the methods and evaluation of a novel optokinetic nystagmus (OKN) based PAD system for mobile device applications which leverages phase-locked temporal features of a unique reflexive behavioral response. Background is provided for historical and literary context of eye motion and ocular tracking to provide context to the objectives and accomplishments of this work. An evaluation of the improved methods for sample processing and sequential stability is provided with highlights for the presented improvements to the stability of convolutional facial landmark localization, and automated spatiotemporal feature extraction and classification models. Insights gleaned from this work are provided to elucidate some of the major challenges of mobile ocular motion feature extraction, as well as additional future considerations for the refinement and application of OKN motion signatures as a novel mobile device based PAD method.
    Table of Contents
    Introduction -- Retrospective, Contextual and Contemporary analysis -- Experimental Design -- Methods and Results -- Discussion -- Conclusions
    URI
    https://hdl.handle.net/10355/80792
    Degree
    Ph.D. (Doctor of Philosophy)
    Thesis Department
    Electrical and Computer Engineering (UMKC)
     
    Computer Science (UMKC)
     
    Collections
    • Computer Science and Electrical Engineering Electronic Theses and Dissertations (UMKC)
    • 2020 UMKC Dissertations - Freely Available Online

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