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Style Aggregated Network for Facial Landmark Detection

We provide the training and testing codes for SAN, implemented in PyTorch.

Preparation

Dependencies

• Python3.6
• PyTorch
• torchvision

Datasets download

• Download 300W-Style and AFLW-Style from Google Drive, and extract the downloaded files into ~/datasets/.
• In 300W-Style and AFLW-Style directories, the Original sub-directory contains the original images from 300-W and AFLW.
• The sketch, light, and gray style images are used to analyze the image style variance in facial landmark detection.

Figure 1. Our 300W-Style and AFLW-Style datasets. There are four styles, original, sketch, light, and gray.

300W-Style Directory

• 300W-Gray : 300W afw helen ibug lfpw
• 300W-Light : 300W afw helen ibug lfpw
• 300W-Sketch : 300W afw helen ibug lfpw
• 300W-Original : 300W afw helen ibug lfpw
• Bounding_Boxes

AFLW-Style Directory

• aflw-Gray : 0 2 3
• aflw-Light : 0 2 3
• aflw-Sketch : 0 2 3
• aflw-Original : 0 2 3
• annotation: 0 2 3

Generate lists for training and evaluation

cd cache_data
python aflw_from_mat.py
python generate_300W.py

The generated list file will be saved into ./cache_data/lists/300W and ./cache_data/lists/AFLW.

Prepare images for training the style-aggregated face generation module

python crop_pic.py

The above commands will pre-crop the face images, and save them into ./cache_data/cache/300W and ./cache_data/cache/AFLW.

Training and Evaluation

300-W

• Step-1 : cluster images into different groups, for example sh scripts/300W/300W_Cluster.sh 0,1 GTB 3.
• Step-2 : use sh scripts/300W/300W_CYCLE_128.sh 0,1 GTB or sh scripts/300W/300W_CYCLE_128.sh 0,1 DET to train SAN on 300-W.

AFLW

• Step-1 : cluster images into different groups, for example sh scripts/AFLW/AFLW_Cluster.sh 0,1 GTB 3.
• Step-2 : use sh scripts/AFLW/AFLW_CYCLE_128.FULL.sh or sh scripts/AFLW/AFLW_CYCLE_128.FRONT.sh to train SAN on AFLW.

Normalization

Figure 2. We use the distance between the outer corners of the eyes, i.e., the 37-th and the 46-th points, for normalization.

Citation

Please cite the following paper in your publications if it helps your research:

@inproceedings{dong2018san,
   title={Style Aggregated Network for Facial Landmark Detection},
   author={Dong, Xuanyi and Yan, Yan and Ouyang, Wanli and Yang, Yi},
   booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
   year={2018},
}

Contact

To ask questions or report issues, please open an issue on the issues tracker.