Resource-aware Facial Recognition: a Hybrid Cloud-edge Computing Approach for Low-end Devices
Year: 2024
Type: article
Abstract: Facial recognition technology has become ubiquitous in modern applications, yet its implementation on resource-constrained devices remains challenging due to computational limitations. This article presents a novel framework for optimizing facial recognition systems on low-end devices through a three-pronged approach: algorithmic simplification, selective frame processing, and hybrid cloud computing integration. The proposed methodology achieves a 64% reduction in computational overhead while maintaining an accuracy rate of 93.7% compared to traditional implementations. Through extensive experimentation across various device configurations (n=127), the article demonstrates that the streamlined processing pipeline, incorporating adaptive frame skipping and cloud-offloading strategies, reduces memory usage by 48% and decreases power consumption by 37% compared to baseline models. The framework's effectiveness was validated through real-world testing on devices with processors ranging from 1.1 GHz to 2.0 GHz and RAM configurations between 1GB and 3GB. Results indicate significant improvements in real-time performance, with response times averaging 156ms compared to 425ms in conventional systems. These findings suggest a viable pathway for democratizing facial recognition technology across a broader spectrum of devices, particularly in regions where high-end hardware accessibility is limited.
Author: Researcher
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911675090
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