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ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment (ICCV 2021)

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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. 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 NMEinter-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 NMEinter-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 NMEinter-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

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ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment (ICCV 2021)

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