Energy-Aware Multimodal Biometric Authentication Systems for Mobile

Abstract

As smartphones become central to personal identity verification, the need for secure, efficient, and power-conscious authentication methods is paramount. While multimodal biometric systems, combining features like face and fingerprint recognition, offer superior accuracy over unimodal approaches, their adoption on mobile platforms is severely hindered by high energy consumption and hardware variability. This paper introduces an energy-aware multimodal biometric authentication framework designed for Android smartphones that directly confronts this challenge. Our system features a novel adaptive fusion mechanism that intelligently balances recognition accuracy with power consumption by dynamically adjusting the weights of biometric modalities in real-time based on battery level and ambient environmental conditions. To validate our framework, we conducted an extensive experimental study involving 46 participants across 460 authentication sessions on five different smartphone models. The results demonstrate that our adaptive system significantly outperforms both unimodal and static fusion baselines. It achieves a high True Acceptance Rate (TAR) and a low Equal Error Rate (EER) while substantially reducing the Energy-Delay Product (EDP). A key feature is the system's ability to gracefully degrade to a secure, fingerprint-only mode when the battery is critically low, ensuring continuous availability without compromising security. This research proves that intelligent, context-aware modality adaptation is a viable strategy for creating robust, efficient, and sustainable biometric authentication solutions suitable for long-term use in consumer electronics.