EdgeFace: Efficient Face Recognition Model for Edge Devices

This repository contain inference code and pretrained models to use EdgeFace: Efficient Face Recognition Model for Edge Devices, which is the winning entry in the compact track of "EFaR 2023: Efficient Face Recognition Competition" organised at the IEEE International Joint Conference on Biometrics (IJCB) 2023. For the complete source code of training and evaluation, please check the official repository.

Installation

$ pip install -r requirements.txt

Inference

The following code shows how to use the model for inference:

import torch
from torchvision import transforms
from face_alignment import align
from backbones import get_model

# load model
model_name="edgeface_s_gamma_05" # or edgeface_xs_gamma_06
model=get_model(model_name)
checkpoint_path=f'checkpoints/{arch}.pt'
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')).eval()

transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
            ])

path = 'path_to_face_image'
aligned = align.get_aligned_face(path) # align face
transformed_input = transform(aligned) # preprocessing

# extract embedding
embedding = model(transformed_input)

Pre-trained models

• EdgeFace-s (gamma=0.5): available in checkpoints/edgeface_s_gamma_05.pt
• EdgeFace-xs (gamma=0.6): available in checkpoints/edgeface_xs_gamma_06.pt

Performance

The performance of each model is reported in Table 2 of the paper:

🚀 New! Using EdgeFace Models via torch.hub

Available Models on torch.hub

• edgeface_base
• edgeface_s_gamma_05
• edgeface_xs_q
• edgeface_xs_gamma_06
• edgeface_xxs
• edgeface_xxs_q

NOTE: Models with _q are quantised and require less storage.

Loading EdgeFace Models with torch.hub

You can load the models using torch.hub as follows:

import torch
model = torch.hub.load('otroshi/edgeface', 'edgeface_xs_gamma_06', source='github', pretrained=True)
model.eval()

Performance benchmarks of different variants of EdgeFace

Model	MPARAMS	MFLOPs	LFW(%)	CALFW(%)	CPLFW(%)	CFP-FP(%)	AgeDB30(%)
edgeface_base	18.23	1398.83	99.83 ± 0.24	96.07 ± 1.03	93.75 ± 1.16	97.01 ± 0.94	97.60 ± 0.70
edgeface_s_gamma_05	3.65	306.12	99.78 ± 0.27	95.55 ± 1.05	92.48 ± 1.42	95.74 ± 1.09	97.03 ± 0.85
edgeface_xs_gamma_06	1.77	154.00	99.73 ± 0.35	95.28 ± 1.37	91.58 ± 1.42	94.71 ± 1.07	96.08 ± 0.95
edgeface_xxs	1.24	94.72	99.57 ± 0.33	94.83 ± 0.98	90.27 ± 0.93	93.63 ± 0.99	94.92 ± 1.15

Reference

If you use this repository, please cite the following paper, which is published in the IEEE Transactions on Biometrics, Behavior, and Identity Science (IEEE T-BIOM). The PDF version of the paper is available as pre-print on arxiv. The complete source code for reproducing all experiments in the paper (including training and evaluation) is also publicly available in the official repository.

@article{edgeface,
  title={Edgeface: Efficient face recognition model for edge devices},
  author={George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien},
  journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
  year={2024}
}