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SR LRFR

PyTorch implementation of Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination.

Citation

If you find this work useful for your research, please consider cite our paper:

@inproceedings{lai2019low,
  title={Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination},
  author={Lai, Shun-Cheung and He, Chen-Hang and Lam, Kin-Man},
  journal={IEEE International Conference on Image Processing},
  pages={1173-1177},
  year={2019},
  month={Sep.}
}

Requirements

  • Python 3 (Anaconda installation is recommended)
  • numpy
  • PyTorch >= 0.4.1
  • torchvision
  • OpenCV 3
  • tqdm (progress bar): pip install tqdm

Tested environment: Ubuntu 16.04 with Python 3.6.5, OpenCV 3.4.1, PyTorch 0.4.1 (CUDA 9.2 & cuDNN 7.1)

Low-resolution face verification experiment

Clone this repository

git clone https://github.com/johnnysclai/SR_LRFR
cd SR_LRFR/

To conduct low-resolution face verification, please download and extract the LFW database and 6,000 pairs file from here. Or, you just run the following commands:

mkdir datasets
cd datasets/
wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
tar -xvzf lfw.tgz
cd ../data
wget http://vis-www.cs.umass.edu/lfw/pairs.txt
cd ..

Now, you have the LFW database in datasets/lfw/ and the 6,000 pairs file data/pairs.txt. We have used MTCNN to detect five facial landmarks, which are saved in data/LFW.csv.

Extract the pre-trained checkpoints (models are compressed into parts as they are too large):

cd pretrained/
# Extract edsr_baseline.pth and edsr_lambda0.5.pth
7z x edsr_baseline.7z.001
7z x edsr_lambda0.5.7z.001
cd ..

Run the following commands to obtain the face verification results from our pre-trained models:

cd src/
bash lfw_verification.sh

You should be able to get the following results:

FNet Method/SRNet 7x6 14x12 28x24 112x96
SphereFace-20 (SFace) Bicubic 59.03 82.75 97.60 99.07
EDSR (lambda=0) 73.42 92.25 98.48 -
EDSR (lambda=0.5) 84.03 94.73 98.85 -

*Note that SphereFace-20 (SFace) model is converted from the official released model using extract-caffe-params.

Training

Please download the CelebA dataset from here and extract it to ../../../Datasets/.

Train with single GPU: python train.py --gpu_ids=0

Train with two GPU: python train.py --gpu_ids=0,1

Final model will be saved in ./checkpoint/master/

References