ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment

Paper link: ICCV 2021

Updated: 04/29/2022

Structure

Folder	Description
conf	Configure files of different training tasks.
lib	Core implementation of all kinds of backbones, data loaders, losses and optimizers.
main.py	Entry script for training, testing or conversion.
trainer.py	Entry script for model training.
tester.py	Entry script for model testing.
evaluate.py	Evaluate script for model NME metrics calculation and visualization.
requirements.txt	The dependency list in python.

Dependencies

Local machine:

• python==3.7.3
• torch==1.6.0
• requirements.txt
• GPU running option requires:
  • Installing CUDA 10.1 (Runtime + SDK) and configuring NVCC root directory into ENVIRONMENT PATH

Resources

Dataset	Dataset	Metadata (train, test)	Model (pytorch, onnx)
COFW	official	google / baidu	google / baidu
300W	official	google / baidu	google / baidu
WFLW	official	google / baidu	google / baidu

Preparation

• Step1: Install dependencies.
• Step2: Clone repo.
• Step3: Download dataset and metadata of each dataset to data/alignment/${dataset} folder.
• Step4: Set the target dataset through configuring the ${data_definition} variable in conf/alignment.py script.
• Step5: Run the scripts below.

Training in local machine

python main.py --mode=train --config_name=alignment --device_ids=0,1,2,3

Testing in local machine

python main.py --mode=test --config_name=alignment --pretrained_weight=${model_path} --device_ids=0

Evaluation in local machine

python evaluate.py --mode=nme --config_name=alignment --model_path=${model_path} --metadata_path==${metadata_path} --image_dir=${image_dir} --device_ids=0

Framework

The framework of ADNet.

Performance

The models trained by ADNet framework achieved SOTA performance in all of COFW, 300W and WFLW datasets.

Table 1. Comparing with state-of-the-art methods on COFW by NME_inter-pupils.

Method	NME	FR10%	AUC10%
Human	5.60	-	-
RCPR	8.50	20.00	-
TCDCN	8.05	-	-
DAC-CSR	6.03	4.73	-
Wu et al	5.93	-	-
Wing	5.44	3.75	-
DCFE	5.27	7.29	0.3586
Awing	4.94	0.99	0.6440
ADNet	4.68	0.59	0.5317

Table 2. Comparing with state-of-the-art methods on 300W by NME_inter-pupils.

Method	Common Subset	Challenging Subset	Fullset
PCD-CNN	3.67	7.62	4.44
CPM+SBR	3.28	7.58	4.10
SAN	3.34	6.60	3.98
LAB	2.98	5.19	3.49
DeCaFA	2.93	5.26	3.39
DU-Net	2.90	5.15	3.35
LUVLi	2.76	5.16	3.23
AWing	2.72	4.52	3.07
ADNet	2.53	4.58	2.93

Table 3. Comparing with state-of-the-art methods on WFLW by NME_inter-ocular.

Method	Testset	Pose Subset	Expression Subset	Illumination Subset	Make-up Subeset	Occlusion Subset	Blur Subset
ESR	11.13	25.88	11.47	10.49	11.05	13.75	12.20
SDM	10.29	24.10	11.45	9.32	9.38	13.03	11.28
CFSS	9.07	21.36	10.09	8.30	8.74	11.76	9.96
DVLN	6.08	11.54	6.78	5.73	5.98	7.33	6.88
LAB	5.27	10.24	5.51	5.23	5.15	6.79	6.12
Wing	5.11	8.75	5.36	4.93	5.41	6.37	5.81
DeCaFA	4.62	8.11	4.65	4.41	4.63	5.74	5.38
Awing	4.36	7.38	4.58	4.32	4.27	5.19	4.96
LUVLi	4.37	-	-	-	-	-	-
ADNet	4.14	6.96	4.38	4.09	4.05	5.06	4.79

Citation

@inproceedings{huang2021adnet,
  title={Adnet: Leveraging error-bias towards normal direction in face alignment},
  author={Huang, Yangyu and Yang, Hao and Li, Chong and Kim, Jongyoo and Wei, Fangyun},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3080--3090},
  year={2021}
}

License

The project is released under the MIT License