This software is developed by Zhenhua Feng from the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. The software is implemented by Matlab and powered by the MatConvNet toolbox.
If you use this software, please cite the following publication:
- Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu. Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks, IEEE Conference on Computer Vision and Patten Recognition (CVPR), Salt Lake City, USA, 2018.
@inproceedings{feng2018wing,
title={Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks},
author={Feng, Zhen-Hua and Kittler, Josef and Awais, Muhammad and Huber, Patrik and Wu, Xiao-Jun},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
year={2018},
pages ={2235-2245},
organization={IEEE}
}- You can download the paper from HERE.
- 2018-06-16: Add the demo code as well as two pretrained CNN6 models on the AFLW dataset with 19 facial landmarks
- 2018-03-29: The pre-trained model and test code are coming soon.
- Results on COFW
| Method | NME(%) | Failure Rate(%) |
|---|---|---|
| CNN6 (Wing+PDB) | 5.44 | 3.75 |
| ResNet50 (Wing+PDB) | 5.07 | 3.16 |
- Results on WFLW
| Metric | Method | FullSet | Pose | Expression | Illumination | Makeup | Occlusion | Blur |
|---|---|---|---|---|---|---|---|---|
| NME(%) | 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 | 6.32 | |
| ResNet50 (Wing+PDB) | 4.99 | 8.43 | 5.21 | 4.88 | 5.26 | 6.21 | 5.81 | |
| Failure Rate (%) | ESR | 35.24 | 90.18 | 42.04 | 30.80 | 38.84 | 47.28 | 41.40 |
| SDM | 29.40 | 84.36 | 33.44 | 26.22 | 27.67 | 41.85 | 35.32 | |
| CFSS | 20.56 | 66.26 | 23.25 | 17.34 | 21.84 | 32.88 | 23.67 | |
| DVLN | 10.84 | 46.93 | 11.15 | 7.31 | 11.65 | 16.30 | 13.71 | |
| LAB | 7.56 | 28.83 | 6.37 | 6.73 | 7.77 | 13.72 | 10.74 | |
| ResNet50 (Wing+PDB) | 5.64 | 23.31 | 4.14 | 4.87 | 8.74 | 11.69 | 7.50 | |
| AUC@0.1 | ESR | 0.2774 | 0.0177 | 0.1981 | 0.2953 | 0.2485 | 0.1946 | 0.2204 |
| SDM | 0.3002 | 0.0226 | 0.2293 | 0.3237 | 0.3125 | 0.2060 | 0.2398 | |
| CFSS | 0.3659 | 0.0632 | 0.3157 | 0.3854 | 0.3691 | 0.2688 | 0.3037 | |
| DVLN | 0.4551 | 0.1474 | 0.3889 | 0.4743 | 0.4494 | 0.3794 | 0.3973 | |
| LAB | 0.5323 | 0.2345 | 0.4951 | 0.5433 | 0.5394 | 0.4490 | 0.4630 | |
| ResNet50 (Wing+PDB) | 0.5585 | 0.3309 | 0.4979 | 0.5631 | 0.5460 | 0.4985 | 0.5010 |
- cnn6_v0_aflw: pre-trained CNN-6 model on the AFLW-FULL dataset, using the CNN6 architecture as shown in the paper
- cnn6_v1_aflw: similar to cnn6_v0 but with doubled filter sizes, which performs better than the original CNN6 but a little bit slower
- Download and install MatConvNet to
pathToMatConvNet/. - Modify the path to MatConvNet in demo.m and run the script
This soft ware is released under the Apache 2.0 license.
Dr Zhenhua Feng
Centre for Vision, Speech and Signal Processing
University of Surrey, Guildford GU2 7XH, United Kingdom