README.md

AVCE_FER

Emotion-aware Multi-view Contrastive Learning for Facial Emotion Recognition (ECCV 2022)

Daeha Kim, Byung Cheol Song

CVIP Lab, Inha University

Real-time demo with pre-trained weights

Requirements

• Python (>=3.7)
• PyTorch (>=1.7.1)
• pretrainedmodels (>=0.7.4)
• cvxpy (>=1.1.15)
• Wandb
• Fabulous (terminal color toolkit)

To install all dependencies, do this.

pip install -r requirements.txt

News

[22.07.10]: Add source code and demo.

[22.07.07]: OPEN official pytorch version of AVCE_FER.

Datasets

1. Download three public benchmarks for training and evaluation (I cannot upload datasets due to the copyright issue).

• AffectNet
• Aff-wild
• Aff-wild2
• AFEW-VA

(For more details visit website)

2. Follow preprocessing rules for each dataset by referring pytorch official custom dataset tutorial.

Pretrained weights

• Check pretrained_weights folder.

  • Weights are trained on AFEW-VA dataset.

  • Weights for demo are trained on multiple VA database (please refer here)

Run

1. Go to /src.

2. Train AVCE.

3. (Or) Execute run.sh

CUDA_VISIBLE_DEVICES=0 python main.py --freq 250 --model alexnet --online_tracker 1 --data_path <data_path> --save_path <save_path>

Arguments	Description
freq	Parameter saving frequency.
model	CNN model for backbone. Choose from 'alexnet', and 'resnet18'.
online_tracker	Wandb on/off.
data_path	Path to load facial dataset.
save_path	Path to save weights.

Real-time demo

1. Go to /AVCE_demo.

2. Run main.py.

• Facial detection and AV FER functionalities are equipped.
• Before that, you have to train and save Encoder.t7 and FC_layer.t7.

Citation

@inproceedings{kim2022emotion,
	title={Emotion-aware Multi-view Contrastive Learning for Facial Emotion Recognition},
	author={Kim, Daeha and Song, Byung Cheol},
	booktitle={European Conference on Computer Vision},
	pages={178--195},
	year={2022},
	organization={Springer}

}

Contact

If you have any questions, feel free to contact me at kdhht5022@gmail.com.