HCPLab-SYSU/CD-FER-Benchmark HCPLab-SYSU/CD-FER-Benchmark

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HCPLab-SYSU/CD-FER-Benchmark
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Repository Details

A Unified Evaluation Benchmark for Cross-Domain Facial Expression Recognition (TPAMI'22, ACM MM'20)

Cross Domain Facial Expression Recognition Benchmark

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Implementation of papers:

Pipeline

Environment

Ubuntu 16.04 LTS, Python 3.5, PyTorch 1.3

Note: We also provide docker image for this project, click here. (Tag: py3-pytorch1.3-agra)

Datasets

To apply for the AFE, please complete the AFE Database User Agreement and submit it to [email protected] or [email protected].

Note:

  1. The AFE Database Agreement needs to be signed by the faculty member at a university or college and sent it by email.
  2. In order to comply with relevant regulations, you need to apply for the image data of the following data sets by yourself, including CK+, JAFFE, SFEW 2.0, FER2013, ExpW, RAF.

Pre-Train Model

You can download pre-train models in OneDrive.

Note: To replace backbone of each methods, you should modify and run getPreTrainedModel_ResNet.py (or getPreTrainedModel_MobileNet.py) in the folder where you want to use the method.

Usage

Before run these script files, you should download datasets and pre-train model, and run getPreTrainedModel_ResNet.py (or getPreTrainedModel_MobileNet.py).

Run ICID

cd ICID
bash Train.sh

Run DFA

cd DFA
bash Train.sh

Run LPL

cd LPL
bash Train.sh

Run DETN

cd DETN
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run FTDNN

cd FTDNN
bash Train.sh

Run ECAN

cd ECAN
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run CADA

cd CADA
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run SAFN

cd SAFN
bash TrainWithSAFN.sh

Run SWD

cd SWD
bash Train.sh

Run AGRA

cd AGRA
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Result

Souce Domain: RAF

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-50 74.42 50.70 48.85 53.70 69.54 59.44
DFA ResNet-50 64.26 44.44 43.07 45.79 56.86 50.88
LPL ResNet-50 74.42 53.05 48.85 55.89 66.90 59.82
DETN ResNet-50 78.22 55.89 49.40 52.29 47.58 56.68
FTDNN ResNet-50 79.07 52.11 47.48 55.98 67.72 60.47
ECAN ResNet-50 79.77 57.28 52.29 56.46 47.37 58.63
CADA ResNet-50 72.09 52.11 53.44 57.61 63.15 59.68
SAFN ResNet-50 75.97 61.03 52.98 55.64 64.91 62.11
SWD ResNet-50 75.19 54.93 52.06 55.84 68.35 61.27
Ours ResNet-50 85.27 61.50 56.43 58.95 68.50 66.13
Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-18 67.44 48.83 47.02 53.00 68.52 56.96
DFA ResNet-18 54.26 42.25 38.30 47.88 47.42 46.02
LPL ResNet-18 72.87 53.99 49.31 53.61 68.35 59.63
DETN ResNet-18 64.19 52.11 42.25 42.01 43.92 48.90
FTDNN ResNet-18 76.74 50.23 49.54 53.28 68.08 59.57
ECAN ResNet-18 66.51 52.11 48.21 50.76 48.73 53.26
CADA ResNet-18 73.64 55.40 52.29 54.71 63.74 59.96
SAFN ResNet-18 68.99 49.30 50.46 53.31 68.32 58.08
SWD ResNet-18 72.09 53.52 49.31 53.70 65.85 58.89
Ours ResNet-18 77.52 61.03 52.75 54.94 69.70 63.19
Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID MobileNet V2 57.36 37.56 38.30 44.47 60.64 47.67
DFA MobileNet V2 41.86 35.21 29.36 42.36 43.66 38.49
LPL MobileNet V2 59.69 40.38 40.14 50.13 62.26 50.52
DETN MobileNet V2 53.49 40.38 35.09 45.88 45.26 44.02
FTDNN MobileNet V2 71.32 46.01 45.41 49.96 62.87 55.11
ECAN MobileNet V2 53.49 43.08 35.09 45.77 45.09 44.50
CADA MobileNet V2 62.79 53.05 43.12 49.34 59.40 53.54
SAFN MobileNet V2 66.67 45.07 40.14 49.90 61.40 52.64
SWD MobileNet V2 68.22 55.40 43.58 50.30 60.04 55.51
Ours MobileNet V2 72.87 55.40 45.64 51.05 63.94 57.78

Souce Domain: AFE

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-50 56.59 57.28 44.27 46.92 52.91 51.59
DFA ResNet-50 51.86 52.70 38.03 41.93 60.12 48.93
LPL ResNet-50 73.64 61.03 49.77 49.54 55.26 57.85
DETN ResNet-50 56.27 52.11 44.72 42.17 59.80 51.01
FTDNN ResNet-50 61.24 57.75 47.25 46.36 52.89 53.10
ECAN ResNet-50 58.14 56.91 46.33 46.30 61.44 53.82
CADA ResNet-50 72.09 49.77 50.92 50.32 61.70 56.96
SAFN ResNet-50 73.64 64.79 49.08 48.89 55.69 58.42
SWD ResNet-50 72.09 61.50 48.85 48.83 56.22 57.50
Ours ResNet-50 78.57 65.43 51.18 51.31 62.71 61.84
Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-18 54.26 51.17 47.48 46.44 54.85 50.84
DFA ResNet-18 35.66 45.82 34.63 36.88 62.53 43.10
LPL ResNet-18 67.44 62.91 48.39 49.82 54.51 56.61
DETN ResNet-18 44.19 47.23 45.46 45.39 58.41 48.14
FTDNN ResNet-18 58.91 59.15 47.02 48.58 55.29 53.79
ECAN ResNet-18 44.19 60.56 43.26 46.15 62.52 51.34
CADA ResNet-18 72.09 53.99 48.39 48.61 58.50 56.32
SAFN ResNet-18 68.22 61.50 50.46 50.07 55.17 57.08
SWD ResNet-18 77.52 59.15 50.69 51.84 56.56 59.15
Ours ResNet-18 79.84 61.03 51.15 51.95 65.03 61.80
Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID MobileNet V2 55.04 42.72 34.86 39.94 44.34 43.38
DFA MobileNet V2 44.19 27.70 31.88 35.95 61.55 40.25
LPL MobileNet V2 69.77 50.23 43.35 45.57 51.63 52.11
DETN MobileNet V2 57.36 54.46 32.80 44.11 64.36 50.62
FTDNN MobileNet V2 65.12 46.01 46.10 46.69 53.02 51.39
ECAN MobileNet V2 71.32 56.40 37.61 45.34 64.00 54.93
CADA MobileNet V2 70.54 45.07 40.14 46.72 54.93 51.48
SAFN MobileNet V2 62.79 53.99 42.66 46.61 52.65 51.74
SWD MobileNet V2 64.34 53.52 44.72 50.24 55.85 53.73
Ours MobileNet V2 75.19 54.46 47.25 47.88 61.10 57.18

Mean of All Methods

Souce Domain: RAF

Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ResNet-50 75.87 54.30 54.49 54.82 62.09 59.51
ResNet-18 69.43 51.88 47.94 51.72 61.26 56.45
MobileNet V2 60.78 45.15 39.59 47.92 56.46 49.98

Souce Domain: AFE

Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ResNet-50 65.41 57.93 47.04 47.26 57.87 55.10
ResNet-18 60.23 56.25 46.95 47.57 58.34 53.87
MobileNet V2 63.57 48.46 40.14 44.91 56.34 50.68

Citation

@article{Chen2022CD-FER,
  author={Chen, Tianshui and Pu, Tao and Wu, Hefeng and Xie, Yuan and Liu, Lingbo and Lin, Liang},
  title={Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning}, 
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  volume={44},
  number={12},
  pages={9887-9903},
  year={2022},
  publisher={IEEE},
  doi={10.1109/TPAMI.2021.3131222}
}

@inproceedings{Xie2020AGRA,
  author={Xie, Yuan and Chen, Tianshui and Pu, Tao and Wu, Hefeng and Lin, Liang},
  title={Adversarial graph representation adaptation for cross-domain facial expression recognition},
  booktitle={Proceedings of the 28th ACM international conference on Multimedia},
  year={2020},
  pages={1255--1264},
  publisher={Association for Computing Machinery},
  doi={10.1145/3394171.3413822}
}

Contributors

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