/CelebA-Spoof

A large-scale face anti-spoofing dataset

CelebA-Spoof

CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination,environment and spoof types. Live image selected from the CelebA dataset. We collect and annotate spoof images of CelebA-Spoof.

Among 43 rich attributes, 40 attributes belong to live images including all facial components and accessories such as skin, nose, eyes, eyebrows, lip, hair, hat, eyeglass. 3 attributes belong to spoof images including spoof types, environments and illumination conditions.

CelebA-Spoof can be used to train and evaluate algorithms of face anti-spoofing.

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[paper]

AENet

Based on these rich attributes, we further propose a simple yet powerful multi-task framework, namely AENet. Through AENet,we conduct extensive experiments to explore the roles of semantic informationand geometric information in face anti-spoofing. CNN4-1

Sample images

attribute stastic-1

CelebA-Spoof Dataset Downloads

Related Works

  • CelebA dataset:
    Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang, "Deep Learning Face Attributes in the Wild", in IEEE International Conference on Computer Vision (ICCV), 2015

Dataset Agreement

  • The CelebA-Spoof dataset is available for non-commercial research purposes only.
  • You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  • You agree not to further copy, publish or distribute any portion of the CelebA-Spoof dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

License and Citation

The use of this software is RESTRICTED to non-commercial research and educational purposes.

@inproceedings{CelebA-Spoof,
  title={CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations},
  author={Zhang, Yuanhan and Yin, Zhenfei and Li, Yidong and Yin, Guojun and Yan, Junjie and Shao, Jing and Liu, Ziwei},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}