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Deep Alignment Network: A convolutional neural network for robust face alignment

This is a Tensorflow implementations of paper "Deep Alignment Network: A convolutional neural network for robust face alignment". You can see Original implementation here.

System

  • No Windows !

Getting started

  • Tensorflow 1.7.0
  • OpenCV 3.1.0 or newer

Train Model

  • Download Datasets.
  • Put images & pts in SAME folder.
  • Write mirror file. There is a 68 landmark mirror file. download
  • Preprocess.
python preprocessing.py --input_dir=... --output_dir=... --istrain=True --repeat=10 --img_size=112 --mirror_file=./Mirror68.txt
  • Train model.
python DAN_V2.py -ds 1 --data_dir=preprocess_output_dir --data_dir_test=...orNone -nlm 68 -te=15 -epe=1 -mode train
python DAN_V2.py -ds 2 --data_dir=preprocess_output_dir --data_dir_test=...orNone -nlm 68 -te=45 -epe=1 -mode train

Eval Acc

  • Download Datasets for test.
  • Put images & pts in SAME folder.
  • Preprocess.
python preprocessing.py --input_dir=... --output_dir=... --istrain=False --img_size=112
  • Eval model Acc.
python DAN_V2.py -ds 2 --data_dir=preprocess_output_dir -nlm 68 -mode eval

Results on 300W

  • Speed : 4ms per Image on GTX 1080 Ti
  • Err : 1.34 % on 300W common subset(bounding box diagonal normalization).

Pre-trained Model

TODO:You can download pre-trained model here. This model trained on 300W dataset.