AAAI2020-RFMetaFAS

Pytorch codes for Regularized Fine-grained Meta Face Anti-spoofing <a href=http://arxiv.org/pdf/1911.10771.pdf> (arxiv) </a> in AAAI 2020

Idea of the proposed regularized fine-grained meta-learning framework. By incorporating domain knowledge as regularization, meta-learning is conducted in the feature space regularized by the domain knowledge supervision. Thus, generalized learning directions are more likely to be found for task of face anti-spoofing. Besides, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios. Thus, more abundant domain shift information of face anti-spoofing task can be exploited.

<img src="./models/motivation.png" width="500">

Overview of proposed framework. We simulate domain shift by randomly dividing original N source domains in each iteration. Supervision of domain knowledge is incorporated via depth estimator to regularize the learning process of feature extractor. Thus, meta learner conducts the meta-learning in the feature space regularized by the auxiliary supervision of domain knowledge.

<img src="./models/framework.png" width="600">

Setup

• Prerequisites: Python3.6, pytorch=0.4.0, Numpy, TensorboardX, Pillow, SciPy, h5py

• The source code folders:

  1. "models": Contains the network architectures suitable for high-order derivatives calculation of network parameters. Please note that FeatExtractor, DepthEstmator and FeatEmbedder in the code are feature extractor, depth estimator and meta learner in the paper, respectively.
  2. "core": Contains the 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_2020_AAAI,
author = {Shao, Rui and Lan, Xiangyuan and Yuen, Pong C.},
title = {Regularized Fine-grained Meta Face Anti-spoofing},
booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},
year = {2020}
}

Contact: ruishao@life.hkbu.edu.hk