Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
This is the demo code for the ECT approach described in Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection.
Requirements
- python 2.7
Instructions
You may need to compile the caffe firstly before you run the demo code. The pre-trained caffemodel could be downloaded from here.
cd caffe/python
for req in $(cat requirements.txt); do pip install $req; done
cd ..
make all
make pycaffe
cd ..
cd landmark_detection
python run_demo.py --imgDir path/to/you/testing/images --model path/to/the/pretrained/caffemodel --verbose True
Citation
If this work is helpful in your research, please cite the following paper.
@article{zhang2018combining,
title={Combining data-driven and model-driven methods for robust facial landmark detection},
author={Zhang, Hongwen and Li, Qi and Sun, Zhenan and Liu, Yunfan},
journal={IEEE Transactions on Information Forensics and Security},
volume={13},
number={10},
pages={2409--2422},
year={2018},
publisher={IEEE}
}
Acknowledgment
The code is developed upon Caffe-heatmap, Menpo, and Menpofit. Thanks to the original authors.
ECT was extended to detect facial landmarks on artistic portraits by Yaniv et al. Have a look at their code here.
