DEEP LEARNING MODELS FOR MOBILE AND WEARABLE BIOMETRICS

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 e ective user authentication mechanisms for securing transactions. Ocular biometrics o ers 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.