  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.
