xiamenwcy / explainablepr

[Python] Code repository for "A Deep Adversarial Framework for Visually Explainable Periocular Recognition", as seen in CVPR 2021 Biometrics Workshop

ExplainablePR

Code for the method proposed in "A Deep Adversarial Framework for Visually Explainable Periocular Recognition", as seen in CVPR 2021 Biometrics Workshop. [Full Paper][Workshop Slides]

Requirements

Please install and verify the presence of the following requirements (as tested):

OpenCV-Python 4.0.1
NumPy 1.19.4
Scikit-image 0.16.2
Pillow 7.2.0
Natsort 7.0.1
PyTorch 1.3.1
Torchvision 0.4.2
Imutils 0.5.3
Matplotlib 3.3.1
CUDA 10.1/10.2

Learning

All trained models and (non-synthetic) datasets are available in Google Drive.

Skip the next steps and jump to the Inference section if you do not wish to train the models again (if so, simply download the trained_models directory and replace the empty one).

Datasets

Download and unzip dataset.zip and mask_rcnn_training_and_test_dataset.zip. Then, place the resulting files from the former in the /dataset directory and the ones from the latter in the /learning/train_mask_rcnn directory.

DenseNet-161

1. Data pre-processing

   Run the run_preprocessing.py script with the "TRAIN_CNN_DISTINGUISH_PAIRS" variable set to True:

   cd learning/prepare_dataset/
   python3 run_preprocessing.py
   

2. Training

   Run the train_cnn.py script:

   cd learning/train_densenet_161/
   python3 train_cnn.py
   

ResNet-18

1. Data pre-processing

   Run the run_preprocessing.py script with the "TRAIN_CNN_SIDE" variable set to True:

   cd learning/prepare_dataset/
   python3 run_preprocessing.py
   

2. Training

   Run the train_cnn_side.py script:

   cd learning/train_resnet_18/
   python3 train_cnn_side.py
   

StyleGAN2

1. Data pre-processing

   Run the run_preprocessing.py script with the "TRAIN_GAN_PAIRS" variable set to True:

   cd learning/prepare_dataset/
   python3 run_preprocessing.py
   

2. Training

   Run the prepare_data.py and train_gan.py scripts:

   cd learning/train_stylegan_2/
   python3 prepare_data.py --out ./outputs/dataset/dataset.lmdb --n_worker 1 --size 256 ../../dataset/data/train/1/
   python3 train_gan.py ./outputs/dataset/dataset.lmdb/ --genuine_or_impostor G
   

Mask R-CNN

1. Data pre-processing

   Run the preprocess_data.py script:

   cd learning/train_mask_rcnn/
   python3 preprocess_data.py
   

2. Training

   Run the periocular_segmentation.py script:

   cd learning/train_mask_rcnn/
   python3 periocular_segmentation.py train --weights coco 
   

Inference

1. Generating the synthetic dataset

   Run the generate_synthetic_dataset.py script:

   cd inference/code/stylegan_2
   python3 generate_synthetic_dataset.py
   

2. Segmenting the synthetic dataset

   Run the get_segmentation_maps.py script:

   cd inference/code/stylegan_2
   python3 get_segmentation_maps.py
   

3. Processing the synthetic dataset

   Run the process_synthetic_dataset.py script:

   cd inference/code/stylegan_2
   python3 process_synthetic_dataset.py
   

4. Recognising and explaining a test pair

   Run the explain_pair.py script (assuming that 2 test images have been placed inside the /inference/images directory):

   cd inference
   python3 explain_pair.py
   

   The scrip above will classify the given pair as being either genuine or impostor and also return a visual explanation that, in the case of a non-match decision, serves as intrinsic justification.

References

If you are using our code, please consider citing our paper:

@InProceedings{Brito_2021_CVPR,
    author    = {Brito, Joao and Proenca, Hugo},
    title     = {A Deep Adversarial Framework for Visually Explainable Periocular Recognition},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {1453-1461}
}