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MMDN-master

A pytorch implementation of "Robust Facial Landmark Detection by Multi-order Multi-constrained Network"

Training

  1. change the path of training and testing datasets to your own paths in main.py.
-imgdirs_train = ['D:/dataset/300W/300W_LP/300W/']
-imgdirs_test_commomset = ['D:/dataset/300W/300W_LP/ibug/']
  1. python ./main.py

Testing

  1. change the path of training and testing datasets to your own paths in demo.py.
imgdirs_test_commomset = ['D:/dataset/ibug/']
  1. python ./demo.py
  2. If you want to use the funtion get_subpixel_from_kpts() to accelerate the testing in demo.py, then you should
cd ./MMDN-master
python setup.py build_ext --inplace

Reference

  1. If the the work or the code is helpful, please cite the following papers
Jun Wan, Zhihui Lai, Jing Li, Jie Zhou, Can Gao, “Robust Facial Landmark Detection by
Multi-order Multi-constraint Deep Networks", IEEE Transactions on Neural Networks and
Learning Systems.

Jun Wan, Zhihui Lai, Jun Liu, Jie Zhou, Can Gao, “Robust Face Alignment by Multi-order
High-precision Hourglass Networks", IEEE Transactions on Image Processing, 2021, 30, pp.
121-133.

Jun Wan, Zhihui Lai, Jie Zhou, Can Gao, Jing Li, “Robust Facial Landmark Detection by
Cross-order Cross-semantic Deep Network", Neural Networks, https://doi.org/10.1016/j.neun-
et.2020.11.001.