Skip to content

Latest commit

 

History

History
42 lines (28 loc) · 1.59 KB

README.md

File metadata and controls

42 lines (28 loc) · 1.59 KB
Raw

CVPR2019-MADDoG

Pytorch codes for Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection in CVPR 2019

The framework of the proposed method:

Setup

  • Prerequisites: Python 3.6, pytorch 0.4.0, Numpy, TensorboardX, Pillow, SciPy, h5py

  • The source code folders:

    1. "models": Contains the network architectures and the definitions of the loss functions.
    2. "core": Contains the pratraining, training and testing files. Note that we generate score for each frame during the testing.
    3. "datasets": Contains datasets loading
    4. "misc": Contains initialization and some preprocessing functions

Training

To run the main file: python main.py --training_type Train

Testing

To run the main file: python main.py --training_type Test

It will generate a .h5 file that contains the score for each frame. Then, we use these scores to calculate the AUC and HTER.

Acknowledge

Please kindly cite this paper in your publications if it helps your research:

@InProceedings{Shao_2019_CVPR,
author = {Shao, Rui and Lan, Xiangyuan and Li, Jiawei and Yuen, Pong C.},
title = {Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Contact: ruishao@comp.hkbu.edu.hk