Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
lib
Jun 6, 2019
Jun 6, 2019
Jun 9, 2019
Jun 6, 2019

Face De-Spoofing: Anti-Spoofing via Noise Modeling

Amin Jourabloo*, Yaojie Liu*, Xiaoming Liu

alt text

Setup

Install the Tensorflow >=1.1, <2.0.

The source code files:

  1. "Architecture.py": Contains the architectures and the definitions of the loss functions.
  2. "data_train.py" : Contains the functions for reading the training data.
  3. "Train.py" : The main training file that read the training data, computes the loss functions and backpropagates error.
  4. "facepad-test.py": It performs the testing on the test videos and generates the score for each frame.

Training

To run the training code: source ~/tensorflow/bin/activate python /data/train_demo/code/Train.py deactivate

Testing

To run the testing code on a test video ("Test_video.avi"):

  1. python facepad-test.py -input Test_video.avi -isVideo 1
  2. It will generate a txt file in the Score folder which contains the score for each frame.

Acknowledge

Please cite the paper:

@inproceedings{eccv18jourabloo,
    title={Face De-Spoofing: Anti-Spoofing via Noise Modeling},
    author={Amin Jourabloo*, Yaojie Liu*, Xiaoming Liu},
    booktitle={In Proceeding of European Conference on Computer Vision (ECCV 2018)},
    address={Munich, Germany},
    year={2018}
}

@inproceedings{eccv18jourabloo,
    title={Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision},
    author={Yaojie Liu*, Amin Jourabloo*, Xiaoming Liu},
    booktitle={In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018)},
    address={Salt Lake City, UT},
    year={2018}
}

If you have any question, please contact: Amin Jourabloo

Releases

No releases published

Packages

No packages published

Languages