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dc.contributor.advisorDerakhshani, Reza
dc.contributor.advisorBeard, Cory
dc.contributor.authorReddy, Narsi
dc.date.issued2020
dc.date.submitted2020 Fall
dc.descriptionTitle from PDF of title page viewed March 4, 2021
dc.descriptionDissertation advisors: Reza Derakhshani and Cory Beard
dc.descriptionVita
dc.descriptionIncludes bibliographical references (page 137-149)
dc.descriptionThesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020
dc.description.abstractOcular 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.
dc.description.tableofcontentsIntroduction -- 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
dc.description.tableofcontentsxxii, 150 pages
dc.identifier.urihttps://hdl.handle.net/10355/80785
dc.subject.lcshBiometric identification
dc.subject.lcshComputer security
dc.subject.lcshEye tracking
dc.subject.lcshMobile computing
dc.subject.lcshMachine learning
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Computer science
dc.titleLearning Efficient Deep Feature Extraction For Mobile Ocular Biometrics
thesis.degree.disciplineElectrical and Computer Engineering (UMKC)
thesis.degree.disciplineTelecommunications and Computer Networking (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)


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