This is an official pytorch implementation of 'Effective Presentation Attack Detection Driven by Face Related Task'. (Accepted by ECCV 2022)
- numpy>=1.17.0
- scipy>=1.5.2
- Pillow>=8.2.0
- pytorch>=1.7.1
- torchvision>=0.8.2
- tqdm>=4.59.0
- scikit-learn>= 0.24.2
The proposed method is evaluated on four publicly-available datasets, i.e.
The proposed FRT-PAD method is trained through three steps:
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Data Preparation
Generate the image list:
python data_find.py \ --data_path {Four paths of saved datasets}For example,
python data_find.py --data_path ['msu_path', 'casia_path', 'idiap_path', 'oulu_path']And then you can get four lists containing images and corresponding labels in './label/' to establish cross-dataset.
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Pre-trained Model Preparation
FRT-PAD method consists of CNN-based PA Detector, Face-Related Tasks and Cross-Modal Adapter. For CNN-based PA Detector (i.e. baseline), the pre-trained model is carried on ImageNet, and you can download the weights from resnet18. For Face-Related Tasks, we applied three different models.
In Face Recognition model, we use a Pre-trained ResNet-18, and you can download the weights from ms1mv3_arcface_r18_fp16/backbone.
In Face Expression Recognition model, we also use a pre-trained ResNet-18, and you can download the weights from SCN.
In Face Attribute Editing model, we only use its Discriminator, which can be downloaded from pretrained-celeba-128x128.
- Training and testing model
python train_main.py \ --train_data [om/ci] --test_data [ci/om] --downstream [FE/FR/FA] --graph_type [direct/dense]