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Efficient Face Recognition Model for Edge Devices


We proposed EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. The proposed EdgeFace model achieved the top ranking among models with fewer than 2M parameters in the IJCB 2023 Efficient Face Recognition Competition.

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.


George, A. and Ecabert, C. and Otroshi Shahreza, H. and Kotwal, K. and Marcel, S. (2023). EFaR 2023: Efficient Face Recognition Competition. IEEE International Joint Conference on Biometrics (IJCB 2023).


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.


  • Face Recognition

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