FERAtt: Facial Expression Recognition with Attention Net

Introduction

Pytorch implementation for FERAtt neural net. Facial Expression Recognition with Attention Net (FERAtt), is based on the dual-branch architecture and consists of four major modules: (i) an attention module $$G_{att}$$ to extract the attention feature map, (ii) a feature extraction module $G_{ft}$ to obtain essential features from the input image $I$, (iii) a reconstruction module $G_{rec}$ to estimate a good attention image $I_{att}$, and (iv) a representation module $G_{rep}$ that is responsible for the representation and classification of the facial expression image.

Prerequisites

• Linux or macOS
• Python 3
• NVIDIA GPU + CUDA cuDNN
• PyTorch 0.4

Installation

$git clone https://github.com/pedrodiamel/pytorchvision.git
$cd pytorchvision
$python setup.py install
$pip install -r installation.txt

Visualize result with Visdom

We now support Visdom for real-time loss visualization during training!

To use Visdom in the browser:

# First install Python server and client 
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server -env_path runs/visdom/
# http://localhost:8097/

How use

Step 1: Train

./train_bu3dfe.sh
./train_ck.sh

Citation

If you find this useful for your research, please cite the following paper.

@article{fernandez2019feratt,
  title={FERAtt: Facial Expression Recognition with Attention Net},
  author={Fernandez, Pedro D Marrero and Pe{\~n}a, Fidel A Guerrero and Ren, Tsang Ing and Cunha, Alexandre},
  journal={arXiv preprint arXiv:1902.03284},
  year={2019}
}

Acknowledgments

Gratefully acknowledge financial support from the Brazilian government agency FACEPE.