In this paper, we present EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achieves excellent face recognition performance optimized for edge devices. The proposed EdgeFace network not only maintains low computational costs and compact storage, but also achieves high face recognition accuracy, making it suitable for deployment on edge devices. Extensive experiments on challenging benchmark face datasets demonstrate the effectiveness and efficiency of EdgeFace in comparison to state-of-the-art lightweight models and deep face recognition models. Our EdgeFace model with 1.77M parameters achieves state of the art results on LFW (99.73%), IJB-B (92.67%), and IJB-C (94.85%), outperforming other efficient models with larger computational complexities. The code to replicate the experiments will be made available publicly.
@article{arxiv.2307.01838,
title = {EdgeFace: Efficient Face Recognition Model for Edge Devices},
author = {Anjith George and Christophe Ecabert and Hatef Otroshi Shahreza and Ketan Kotwal and Sebastien Marcel},
journal= {arXiv preprint arXiv:2307.01838},
year = {2024}
}
Comments
11 pages, Accepted for publication in IEEE Transactions on Biometrics, Behavior, and Identity Science