# LaPa-Dataset for face parsing (unofficial mirror)
## Introduction
we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale **La**ndmark guided face **Pa**rsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.


## Citation
If you use our datasets, please cite the following paper:

[A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.](https://aaai.org/ojs/index.php/AAAI/article/view/6832/6686) Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei. In AAAI, 2020.

```
@inproceedings{liu2020new,  
  title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.},  
  author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao},  
  booktitle={AAAI},  
  pages={11637--11644},  
  year={2020}  
}
```

## License
This LaPa Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.