AdaptiveWingLoss

arXiv

Pytorch Implementation of Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression.

Update Logs:

October 28, 2019

Pretrained Model and evaluation code on WFLW dataset is released.

Installation

Note: Code was originally developed under Python2.X and Pytorch 0.4. This released version was revisioned from original code and was tested on Python3.5.7 and Pytorch 1.3.0.

Install system requirements:

sudo apt-get install python3-dev python3-pip python3-tk libglib2.0-0

Install python dependencies:

pip3 install -r requirements.txt

Run Evaluation on WFLW dataset

Download and process WFLW dataset
- Download WFLW dataset and annotation from Here.
- Unzip WFLW dataset and annotations and move files into ./dataset directory. Your directory should look like this:
```
AdaptiveWingLoss
└───dataset
   │
   └───WFLW_annotations
   │   └───list_98pt_rect_attr_train_test
   │   │
   │   └───list_98pt_test
   │
   └───WFLW_images
       └───0--Parade
       │
       └───...
```
- Inside ./dataset directory, run:
```
python convert_WFLW.py
```
  A new directory ./dataset/WFLW_test should be generated with 2500 processed testing images and corresponding landmarks.
Download pretrained model from Google Drive and put it in ./ckpt directory.
Within ./Scripts directory, run following command:
```
sh eval_wflw.sh
```
*GTBbox indicates the ground truth landmarks are used as bounding box to crop faces.

Future Plans

Release evaluation code and pretrained model on WFLW dataset.
Release training code on WFLW dataset.
Release pretrained model and code on 300W, AFLW and COFW dataset.
Replease facial landmark detection API

Citation

If you find this useful for your research, please cite the following paper.

@InProceedings{Wang_2019_ICCV,
author = {Wang, Xinyao and Bo, Liefeng and Fuxin, Li},
title = {Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

Acknowledgments

This repository borrows or partially modifies hourglass model and data processing code from face alignment and pose-hg-train.