Code for the paper "An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification".
git clone https://github.com/OpenImagingLab/LenslessFace.git
cd LenslessFace
conda create -n lenslessface python=3.9
conda activate lenslessface
pip install -r requirements.txt
For training, we use the Asian-Celeb dataset. Tests are conducted on the LFW dataset and FCFD dataset , which should be downloaded and extracted to the data directory.
You can modify the arguments in config_file
to change the dataset path.
For RGB-based teacher model training, run the following command:
./scripts/dist_train_teacher.sh config_file
An example of config_file
is configs/face_no_optical/rgb_teacher.py
.
For lensless-based student model training, run the following command:
./scripts/dist_train.sh config_file
An example of config_file
is configs/distill/face/base.py
.
For lensless-based face center detection model training, run the following command:
./scripts/dist_train_pose.sh config_file
An example of config_file
is configs/face_center_detection/base.py
.
For aligned face verification, run the following command:
./scripts/test.sh config_file check_point_path
An example of config_file
is configs/distil/face/base.py
.
For random face verification, run the following command:
./scripts/test_random.sh config_file
An example of config_file
is configs/hybrid/optical/base_test.py
.
you need to modify the cls_checkpoint
and face_center_detection_checkpoint
in the config_file
.
We thank the authors and maintainers of the following repositories for providing the frameworks and datasets that significantly facilitated our research:
Special thanks also go to the authors of the datasets we used for training and evaluation.
Citation Please cite our paper if you find this repository useful for your research:
@misc{cai2024lenslessface,
title={LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification},
author={Xin Cai and Hailong Zhang and Chenchen Wang and Wentao Liu and Jinwei Gu and Tianfan Xue},
year={2024},
eprint={2406.04129},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
This project is licensed under the terms of the MIT license.