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FFR-Net
The work of "A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification".
Introduction
We introduce a unified framework, named Face Feature Rectification Network (FFR-Net), for masked and mask- free face recognition. Experiments show that our frame- work achieves the best average performance on mixed datasets (mask and mask-free faces) with a single model. Besides, we propose rectification blocks (RecBlocks) to rectify features of masked or mask-free faces in both spatial and channel dimensions. RecBlocks can maximize the consistency between masked faces and their mask-free counterparts in the rectified feature space.
Environment
The work is with Python 3.6 and PyTorch 1.7.
Pretrained Models
We provide 2 pretrained models:
| models | pretrained file | mask-free | masked |
|---|---|---|---|
| SENet50 | se50.pth | ✔ | ✘ |
| FFR-Net | FFRNet.pth | ✔ | ✔ |
Our model is trained on augmented CASIA-WebFace dataset. Note that the pretrained weight of SENet is fixed during the training process.
Data
We provide masked CASIA-WebFace and masked LFW datasets (already aligned) and corresponding txt files:
| CASIA-WebFace dataset | CASIA-WebFace clean list | LFW dataset | LFW pair list |
|---|---|---|---|
| casia_masked.tar.gz | casia_cleanlist.txt | lfw_masked.tar.gz | lfw_pairs.txt |
Quick start
Type the following commands to train the model:
python3 run.py
License
This work is under MIT License.
Citation
If you find this code useful in your research, please consider citing:
@inproceedings{hao2022unified,
title={A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification},
author={Hao, Shaozhe and Chen, Chaofeng and Chen, Zhenfang and Wong, Kwan-Yee~K.},
booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
year={2022}
}