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Deep learning models for mobile and wearable biometrics
Almadan, Ali
Almadan, Ali
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dissertation
Adobe PDF, 4.66 MB
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2023-05
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Dissertation
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Abstract
The mobile technology revolution has transformed mobile devices from communication tools
to all-in-one platforms. As a result, more people are using smartphones to access e-commerce
and banking services, replacing traditional desktop computers. However, smartphones are more
prone to being lost or stolen, requiring effective user authentication mechanisms for securing
transactions. Ocular biometrics offers accuracy, security, and ease of use on mobile devices for
user authentication. In addition, face recognition technology has been widely adopted in
intelligence gathering, law enforcement, surveillance, and consumer applications. This
technology has recently been implemented in smartphones and body-worn cameras (BWC) for
surveillance and situational awareness. However, these high-performing models require
significant computational resources, making their deployment on resource-constrained
smartphones challenging. To address this challenge, studies have proposed compact-size
ocular-based deep-learning models for on-device deployment. In this context, we conduct a
thorough analysis of existing neural network compression techniques applied standalone and in
combination for ocular-based user authentication and facial recognition.
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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
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Wichita State University
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© Copyright 2023 by Ali Almadan
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