DARK FACE: Face Detection in Low Light Condition

Spatial and Temporal Restoration, Understanding and Compression Team
Wangxuan institute of computer technology, Peking University

News

  • 2019-02-14 The dataset is being prepared.
  • 2019-03-15 The dataset is released.
  • 2019-03-26 Dataet updated. The issue that one image in the training set cannot be read has been addressed.
  • 2019-05-12 New versions of evaluation tools online. Some bugs are fixed.
  • 2021-06-14 Please refer to [UG2+ Challenge] for the lastest progress.

Description

DARK FACE dataset provides 6,000 real-world low light images captured during the nighttime, at teaching buildings, streets, bridges, overpasses, parks etc., all labeled with bounding boxes for of human face, as the main training and/or validation sets. We also provide 9,000 unlabeled low-light images collected from the same setting. Additionally, we provided a unique set of 789 paired low-light/normal-light images captured in controllable real lighting conditions (but unnecessarily containing faces), which can be used as parts of the training data at the participants' discretization. There will be a hold-out testing set of 4,000 low-light images, with human face bounding boxes annotated.

Download

Below we list other detection datasets in the degraded condition. A more detailed comparison of the datasets can be found in the paper.

  • UFDD dataset: UFDD is proposed for face detection in adverse condition including weather-based degradations, motion blur, focus blur and several others.
  • Exclusively Dark dataset: Exclusively Dark dataset consists exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.

Citation

@ARTICLE{poor_visibility_benchmark,
  author={Yang, Wenhan and Yuan, Ye and Ren, Wenqi and Liu, Jiaying and Scheirer, Walter J. and Wang, Zhangyang and Zhang, and et al.},
  journal={IEEE Transactions on Image Processing}, 
  title={Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study}, 
  year={2020},
  volume={29},
  number={},
  pages={5737-5752},
  doi={10.1109/TIP.2020.2981922}
}

@inproceedings{Chen2018Retinex,
       title={Deep Retinex Decomposition for Low-Light Enhancement},
       author={Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu},
       booktitle={British Machine Vision Conference},
       year={2018},
}

Contact

For questions and result submission, please contact Wenhan Yang at yangwenhan@pku.edu.com

The website codes are borrowed from WIDER FACE Website.