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.

翻译：本文提出EdgeFace——一种受EdgeNeXt混合架构启发的轻量高效人脸识别网络。通过有效结合CNN与Transformer模型的双重优势以及低秩线性层，EdgeFace实现了针对边缘设备优化的卓越人脸识别性能。所提出的EdgeFace网络不仅保持低计算成本与紧凑存储需求，还取得了高精度人脸识别效果，适合部署在边缘设备上。在具有挑战性的基准人脸数据集上的大量实验表明，与最先进的轻量级模型及深度人脸识别模型相比，EdgeFace兼具高效性与有效性。参数量为1.77M的EdgeFace模型在LFW（99.73%）、IJB-B（92.67%）和IJB-C（94.85%）上取得领先结果，超越了计算复杂度更高的其他高效模型。用于复现实验的代码将公开发布。