Implementation of PFLD For 68 Facial Landmarks By Tensorflow

• WFLW Dataset

  Wider Facial Landmarks in-the-wild (WFLW) is a new proposed face dataset. It contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks.

  1.Training and Testing images[Google Drive][Baidu Drive], Unzip and put to ./data/WFLW/raw/

  2.Have got list_68pt_rect_attr_train.txt and list_68pt_rect_attr_test.txt. If you want to get them by youself, please watch get68psFrom98psWFLW.py and run it before please get WFLW Face Annotations , unzip and put to ./data/WFLW/

  3.move Mirror68.txt to ./data/WFLW/annotations/

   $ cd ./data/WFLW 
   $ python3 WFLW_SetPreparation68.py

• 300W Dataset

  300W is a very general face alignment dataset. It has a total of 3148+689 images, each image contains more than one face, but only one face is labeled for each image.File directory includes afw(337)，helen(train 2000+test 330)，ibug(135)，lfpw(train 811+test 224) with 68 fully manual annotated landmarks.

  1.Training and Testing images[Databases][Baidu Drive], Unzip and put to ./data/300W/raw/

  2.Have got list_68pt_rect_attr_train.txt and list_68pt_rect_attr_test.txt. If you want to get them by youself, please watch get68pointsfor300W.py and run it

  3.move Mirror68.txt to ./data/300W/annotations/

   $ cd ./data/300W 
   $ python3 300W_SetPreparation68.py

• 300VW Dataset

• All Dataset

  After completing the steps of each data set above, you can run the code merge_files.py directly .

   $ cd ./data
   $ python3 merge_files.py

training :

 $ sh train.sh

testing:

 $ python3 camera.py

PFLD: A Practical Facial Landmark Detector https://arxiv.org/pdf/1902.10859.pdf

Tensorflow Implementation for 98 Facial Landmarks: https://github.com/guoqiangqi/PFLD