Shared more. Cited more. Safe forever.
    • advanced search
    • submit works
    • about
    • help
    • contact us
    • login
    View Item 
    •   MOspace Home
    • 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
    • View Item
    •   MOspace Home
    • 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
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    advanced searchsubmit worksabouthelpcontact us

    Browse

    All of MOspaceCommunities & CollectionsDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis SemesterThis CollectionDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis Semester

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular AuthorsStatistics by Referrer

    Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics

    Reddy, Narsi
    View/Open
    [PDF] Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics (4.751Mb)
    Date
    2020
    Metadata
    [+] Show full item record
    Abstract
    Ocular biometrics uses physical traits from eye regions such as iris, conjunctival vasculature, and periocular for recognizing the person. Ocular biometrics has gained popularity amongst research and industry alike for its identification capabilities, security, and simplicity in the acquisition, even using a mobile phone's selfie camera. With the rapid advancement in hardware and deep learning technologies, better performances have been obtained using Convolutional Neural Networks(CNN) for feature extraction and person recognition. Most of the early works proposed using large CNNs for ocular recognition in subject-dependent evaluation, where the subjects overlap between the training and testing set. This is difficult to scale for the large population as the CNN model needs to be re-trained every time a new subject is enrolled in the database. Also, many of the proposed CNN models are large, which renders them memory intensive and computationally costly to deploy on a mobile device. In this work, we propose CNN based robust subject-independent feature extraction for ocular biometric recognition, which is memory and computation efficient. We evaluated our proposed method on various ocular biometric datasets in the subject-independent, cross-dataset, and cross-illumination protocols.
    Table of Contents
    Introduction -- Previous Work -- Calculating CNN Models Computational Efficiency -- Case Study of Deep Learning Models in Ocular Biometrics -- OcularNet Model -- OcularNet-v2: Self-learned ROI detection with deep features -- LOD-V: Large Ocular Biometrics Dataset in Visible Spectrum -- Conclusion and Future Work -- Appendix A. Supplementary Materials for Chapter 4 -- Appendix B. Supplementary Materials for Chapter 5 -- Appendix C.Supplementary Materials for Chapter 6 -- Appendix D. Supplementary Materials for Chapter 7
     
    xxii, 150 pages
     
    URI
    https://hdl.handle.net/10355/80785
    Degree
    Ph.D. (Doctor of Philosophy)
    Thesis Department
    Electrical and Computer Engineering (UMKC)
     
    Telecommunications and Computer Networking (UMKC)
     
    Collections
    • Computer Science and Electrical Engineering Electronic Theses and Dissertations (UMKC)
    • 2020 UMKC Dissertations - Freely Available Online

    If you encounter harmful or offensive content or language on this site please email us at harmfulcontent@umkc.edu. To learn more read our Harmful Content in Library and Archives Collections Policy.

    Send Feedback
    hosted by University of Missouri Library Systems
     

     


    If you encounter harmful or offensive content or language on this site please email us at harmfulcontent@umkc.edu. To learn more read our Harmful Content in Library and Archives Collections Policy.

    Send Feedback
    hosted by University of Missouri Library Systems