aman's Introduction

A Novel Micro-Expression Recognition Approach Using Attention-Based Magnification-Adaptive Networks

Mengting Wei, Wenming Zheng, Yuan Zong, Xingxun Jiang, Cheng Lu, Jiateng Liu

In ICASSP 2022 (Download the paper [here])

Directory Structure

project
├── README.md
├── dataset
│   └── load_dataset.py - script to load the dataset for training
├── extract_features
│   ├── resnet.py - script of the Resnet-18 model
│   └── resnet_features.py - script of extracting ME features
├── main
│   ├── config.py - check & change configurations here
│   ├── network.py - AMAN model
│   └── train.py - main script for training and testing
├── utils
│   └── util.py - script of some utils
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Dependices

Current version is tested on:

Windows 10 Pro, 64 bit with CUDA 11.4
python==3.7.5
torch==1.10.2
torchvision==0.11.3
numpy==1.21.6

Model

Dataset Preparation

We use CASME II, SAMM and SMIC-HS for training.
- Magnify the original frames according to the paper, you can use any techniques such as Eulerian Video Magnification, Lagrangian motion magnification or Learning-based Magnification. We recommend you to use the last one since it can produce less artifacts.
To reduce computational cost, extract the features with some former layers of the Resnet-18 and use the features for training and testing. The model is pre-trained on FER+.
Demo of magnified images:

Citation

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{wei2022novel,
  title={A Novel Micro-Expression Recognition Approach Using Attention-Based Magnification-Adaptive Networks},
  author={Wei, Mengting and Zheng, Wenming and Zong, Yuan and Jiang, Xingxun and Lu, Cheng and Liu, Jiateng},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={2420--2424},
  year={2022},
  organization={IEEE}
}

If you have any confusions about the code feel free to contact [email protected].

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aman's Issues

Model

Dear author, due to the lack of data sets, but I am very interested in your work. I wonder if you can provide me with a pre-trained model for reference. Thank you very much! If possible, please contact me with : [email protected]

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