Official Pytorch implementation for "End2End Occluded Face Recognition by Masking Corrupted Features, TPAMI 2021"

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Overview

End2End Occluded Face Recognition by Masking Corrupted Features

This is the Pytorch implementation of our TPAMI 2021 paper End2End Occluded Face Recognition by Masking Corrupted Features.
Haibo Qiu, Dihong Gong, Zhifeng Li, Wei Liu and Dacheng Tao

Requirements

Main packages:

  • python=3.6.7
  • pytorch=1.8.1
  • torchvision=0.9.1
  • cudatoolkit=10.2.89
  • lmdb=1.2.0
  • pyarrow=0.17.0

Or directly create a conda env with

conda env create -f environment.yml

Data preparation

  1. Training data (data/datasets) and pretrained models (pretrained/) can be found here.

  2. Please refer to data/generate_lmdb.py for the lmdb file generation of training data.

  3. Please refer to data/generate_occ_lfw.py for the occluded testing images generation.

Training

Simply run the following script:

bash start.sh

Testing

  1. To reproduce the results in our paper, please download the pretrained models and put them in pretrained/, then run:
bash eval.sh

  1. For megaface testing, the related commonds are included in eval.sh. Current lib/core/megaface_mp.py generates npy file for each sample, which can be evaluated with FaceX-Zoo. Or you can switch the generated function in lib/core/megaface_mp.py to produce bin file and use official devkit for evaluation.

  2. The AR Face dataset evaluation scripts are also included in eval.sh

Acknowledgement

The code is partially developed from PDSN. The occluders images are also from PDSN.

Citation

If you use our code or models in your research, please cite with:

@article{qiu2021end2end,
  title={End2End occluded face recognition by masking corrupted features},
  author={Qiu, Haibo and Gong, Dihong and Li, Zhifeng and Liu, Wei and Tao, Dacheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}
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Comments
  • training process

    training process

    Hello, i got questions for the training process.

    1. i just want to confirm that the provided baseline model is from the first training stated in the paper, right?
    2. can we run start.sh for the second training to reproduce results in the paper?

    thanks in advance.

    opened by jiejie22997 7
  • Megaface datasets

    Megaface datasets

    Hello,

    Thanks for sharing the codes. I want to test on Megaface datasets. How I can access to this datasets? In the official website announcement, Megaface data are no longer being distributed. Thanks!

    opened by Carinazhao22 6
  • How many images are there in one training epoch?

    How many images are there in one training epoch?

    Your paper says that 'Mix-WebFace is the mixup of Occ-WebFace and WebFace with the ratio 2:1'. How many images in the Occ-WebFace? How many images in one training epoch after mixing up Occ-WebFace and WebFace?

    opened by ygtxr1997 2
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Haibo Qiu
Haibo Qiu
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