Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings

2026-06-13

AI Summary

This work proposes a lightweight edge-based face recognition framework leveraging a vector-quantized variational autoencoder (VQ-VAE) to overcome the limitations of conventional approaches that rely on computationally intensive cloud models and are thus unsuitable for low-power edge devices. By integrating VQ-VAE with pretrained face embeddings and incorporating a knowledge distillation mechanism, the method generates compact yet semantically rich identity representations in the latent space. The resulting model significantly reduces computational, storage, and communication overhead while maintaining recognition accuracy comparable to state-of-the-art methods. This approach offers a practical and energy-efficient solution for deploying high-performance face recognition directly on resource-constrained edge platforms.

Abstract

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.

Problem

• sustainable face recognition
• low-power devices
• edge computing
• carbon footprint
• energy consumption

Innovation

• VQ-VAE
• edge computing
• face recognition
• knowledge distillation
• sustainable AI