Cross Domain Facial Expression Recognition Benchmark

Implementation of papers:

• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
  IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2022.
  Tianshui Chen*, Tao Pu*, Hefeng Wu, Yuan Xie, Lingbo Liu, Liang Lin. (* equally contributed)

• Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
  ACM International Conference on Multimedia (ACM MM), 2020. (Oral Presentation)
  Yuan Xie, Tianshui Chen, Tao Pu, Hefeng Wu, Liang Lin.

Environment

Ubuntu 16.04 LTS, Python 3.5, PyTorch 1.3

Note: We also provide a 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.

Usage

Before running 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 the Model to the 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 the Model to the Target Domain

Run CADA

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

For any questions, feel free to open an issue or contact us:

• [email protected]
• [email protected]
• [email protected]