Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

FAU

Implementation of the paper:

Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution. Yingruo Fan, Jacqueline C.K. Lam and Victor O.K. Li. AAAI 2020 [PDF]

The Pytorch version

Overview

Environment

• Ubuntu 18.04.4
• Python 3.7
• Tensorflow 1.14.0

Dependencies

Check the packages needed or simply run the command

❱❱❱ pip install -r requirements.txt

Datasets

For data preparation, please make a request for the BP4D database and the DISFA database.

Data Preprocessing

The Dlib library is utilized to locate the 68 facial landmarks for defining AU locations. The face images are aligned and resized to 256*256 pixels. For annotation files, you need to convert them into json format and make them look like [{imgpath:" ", AUs:[AU1_coord_x,AU1_coord_y,AU1_intensity, ...]}, ...]. An example is provided in examples/train_example.json.

Backbone Model

The backbone model is initialized from the pretrained ResNet-V1-50. Please download it under ${DATA_ROOT}. You can change default path by modifying config.py.

Training

❱❱❱ python train.py --gpu 1

Testing

❱❱❱ python test.py --gpu 1 --epoch *

Citation

@inproceedings{fan2020fau,
    title = {Facial Action Unit Intensity Estimation via Semantic 
    Correspondence Learning with Dynamic Graph Convolution},
    author = {Fan, Yingruo and Lam, Jacqueline and Li, Victor},
    booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence},
    year={2020}
}