RESOURCE-AWARE FACIAL RECOGNITION: A HYBRID CLOUD-EDGE COMPUTING APPROACH FOR LOW-END DEVICES

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.