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High-resolution networks (HRNets) for facial landmark detection

News

• [2020/03/13] Our paper is accepted by TPAMI: Deep High-Resolution Representation Learning for Visual Recognition.

Introduction

This is the official code of High-Resolution Representations for Facial Landmark Detection. We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions, leading to stronger representations. The output representations are fed into classifier. We evaluate our methods on four datasets, COFW, AFLW, WFLW and 300W.

Performance

ImageNet pretrained models

HRNetV2 ImageNet pretrained models are now available! Codes and pretrained models are in HRNets for Image Classification

We adopt HRNetV2-W18(#Params=9.3M, GFLOPs=4.3G) for facial landmark detection on COFW, AFLW, WFLW and 300W.

COFW

The model is trained on COFW train and evaluated on COFW test.

AFLW

The model is trained on AFLW train and evaluated on AFLW full and frontal.

WFLW

300W

Quick start

Environment

This code is developed using on Python 3.6 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are performed using 1 NVIDIA P40 GPU with CUDA 9.0 and cuDNN 7.0. Other platforms or GPUs are not fully tested.

Install

1. Install PyTorch 1.0 following the official instructions
2. Install dependencies

pip install -r requirements.txt

3. Clone the project

git clone https://github.com/HRNet/HRNet-Facial-Landmark-Detection.git

HRNetV2 pretrained models

cd HRNet-Facial-Landmark-Detection
# Download pretrained models into this folder
mkdir hrnetv2_pretrained

Data

1. You need to download the annotations files which have been processed from OneDrive, Cloudstor, and BaiduYun(Acess Code:ypxg).

2. You need to download images (300W, AFLW, WFLW) from official websites and then put them into images folder for each dataset.

Your data directory should look like this:

HRNet-Facial-Landmark-Detection
-- lib
-- experiments
-- tools
-- data
   |-- 300w
   |   |-- face_landmarks_300w_test.csv
   |   |-- face_landmarks_300w_train.csv
   |   |-- face_landmarks_300w_valid.csv
   |   |-- face_landmarks_300w_valid_challenge.csv
   |   |-- face_landmarks_300w_valid_common.csv
   |   |-- images
   |-- aflw
   |   |-- face_landmarks_aflw_test.csv
   |   |-- face_landmarks_aflw_test_frontal.csv
   |   |-- face_landmarks_aflw_train.csv
   |   |-- images
   |-- cofw
   |   |-- COFW_test_color.mat
   |   |-- COFW_train_color.mat  
   |-- wflw
   |   |-- face_landmarks_wflw_test.csv
   |   |-- face_landmarks_wflw_test_blur.csv
   |   |-- face_landmarks_wflw_test_expression.csv
   |   |-- face_landmarks_wflw_test_illumination.csv
   |   |-- face_landmarks_wflw_test_largepose.csv
   |   |-- face_landmarks_wflw_test_makeup.csv
   |   |-- face_landmarks_wflw_test_occlusion.csv
   |   |-- face_landmarks_wflw_train.csv
   |   |-- images

Train

Please specify the configuration file in experiments (learning rate should be adjusted when the number of GPUs is changed).

python tools/train.py --cfg <CONFIG-FILE>
# example:
python tools/train.py --cfg experiments/wflw/face_alignment_wflw_hrnet_w18.yaml

Test

python tools/test.py --cfg <CONFIG-FILE> --model-file <MODEL WEIGHT> 
# example:
python tools/test.py --cfg experiments/wflw/face_alignment_wflw_hrnet_w18.yaml --model-file HR18-WFLW.pth

Other applications of HRNets (codes and models):

• Human pose estimation
• Semantic segmentation
• Object detection
• Image classification

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal   = {TPAMI}
  year={2019}
}

Reference

[1] Deep High-Resolution Representation Learning for Visual Recognition. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. Accepted by TPAMI. download