Exposing.ai
Flickr Diverse Faces

Flickr Diverse Faces Dataset

Flickr Diverse Faces dataset is a subset of the YFCC100M dataset. It was created for the purpose of studying face anonymization for the paper "Deep Privacy" 1

Flickr Diverse Faces (FDF) is a dataset with 1.5M faces "in the wild". FDF has a large diversity in terms of facial pose, age, ethnicity, occluding objects, facial painting, and image background. The dataset is designed for generative models for face anonymization, and it was released with the paper "DeepPrivacy: A Generative Adversarial Network for Face Anonymization.

The dataset was crawled from the website Flickr (YFCC-100M dataset) and automatically annotated. Each face is annotated with 7 facial landmarks (left/right ear, lef/right eye, left/right shoulder, and nose), and a bounding box of the face. Our paper goes into more detail about the automatic annotation.

The images are collected from images in the YFCC-100M dataset and each image in our dataset is free to use for academic or open source projects. For each face, the corresponding original license is given in the metadata. Some of the images require giving proper credit to the original author, as well as indicating any changes that were made to the images. The original author is given in the metadata.

FDF Copyright Distribution

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FDF Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

FDF Creative Commons License Distribution

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FDF Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

FDF Image Upload Year Distribution

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FDF Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

Top 10 FDF Image #Tags

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Top 10 image #tags used in FDF | Download Data (CSV) | Download Chart (SVG)

Top 10 Geocoded Cities FDF

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Top 10 cities for geocoded photos in FDF | Download Data (CSV) | Download Chart (SVG)

Citing This Work

If you reference or use any data from the Exposing.ai project, cite our original research as follows:

@online{Exposing.ai,
  author = {Harvey, Adam. LaPlace, Jules.},
  title = {Exposing.ai},
  year = 2021,
  url = {https://exposing.ai},
  urldate = {2021-01-01}
}

If you reference or use any data from FDF cite the author's work:

@article{Hukkels2019DeepPrivacyAG,
    author = "Hukkel{\aa}s, H{\aa}kon and Mester, Rudolf and Lindseth, Frank",
    title = "DeepPrivacy: A Generative Adversarial Network for Face Anonymization",
    journal = "ArXiv",
    year = "2019",
    volume = "abs/1909.04538"
}

References

  • 1 aHåkon Hukkelås, et al. "DeepPrivacy: A Generative Adversarial Network for Face Anonymization". ArXiv abs/1909.04538. (2019):