Face recognition has become a cornerstone of modern AI applications, yet conventional approaches often rely on computationally intensive models deployed in cloud environments, leading to increased network traffic, high energy consumption, and a heavy carbon footprint. This work introduces a sustainable, edge-deployable face recognition framework based on Vector-Quantized Variational Autoencoders (VQ-VAE), which generates compact and semantically rich latent representations of facial images. By leveraging the compression capacity and reconstruction quality of VQ-VAE embeddings on the edge and combining them with the power of pre-trained face embeddings in a knowledge distillation setup, our system achieves comparable accuracy to state-of-the-art face embedding models while significantly reducing memory and computation requirements on the edge, making it suitable for low-power edge devices. The integration of VQ-VAE compression minimizes network overhead while keeping the matching accuracy high by retaining only the most informative facial features in the latent space. As a result, the reconstructed images preserve the key identity characteristics, improving the robustness and overall performance of the face embeddings.

翻译：人脸识别已成为现代人工智能应用的基石，然而传统方法往往依赖部署于云端的高计算量模型，导致网络流量激增、能耗高企以及沉重的碳足迹。本文提出一种可持续的边缘可部署人脸识别框架，该框架基于向量量化变分自编码器（VQ-VAE），生成紧凑且语义丰富的人脸图像潜在表示。通过利用边缘端VQ-VAE嵌入的压缩能力与重建质量，并结合知识蒸馏框架中预训练人脸嵌入的强大功能，本系统在实现与最先进人脸嵌入模型相当的精度的同时，显著降低了边缘端的内存与计算需求，使其适用于低功耗边缘设备。VQ-VAE压缩的集成最大限度地减少了网络开销，同时通过在潜在空间中仅保留最具信息量的人脸特征，保持了较高的匹配精度。因此，重建图像保留了关键的身份特征，从而提升了人脸嵌入的鲁棒性与整体性能。