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IMDb-Face
2D Box
Classification
Face
|...
License: Unknown

Overview

IMDb-Face is a new large-scale noise-controlled dataset for face recognition research.
The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from
2.0 million raw images. All images are obtained from the IMDb website. A detailed introduction
of IMDb-Face can be found in the paper click here.

We hope that the
IMDb-Face dataset could shed lights on the influences of data noise to the face recognition
task, and point to potential labelling strategies to mitigate some of the problems. It could
serve as a relatively clean data to facilitate future studies of noises in large-scale face
recognition.

Citation

If you find IMDb-Face useful in your research, please cite:

@article{wang2018devil,
title={The Devil of Face Recognition is in the Noise},
author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian,
Chen and Loy, Chen Change},
journal={arXiv preprint arXiv:1807.11649},
year={2018}
}
Data Summary
Type
Image,
Amount
1700K
Size
--
Provided by
Sensetime
SenseTime is a leading global company focused on developing AI technologies that advance the world’s economies, society and humanity for a better tomorrow. It is also the world’s most-funded AI pure-play with the highest valuation.
| Amount 1700K | Size --
IMDb-Face
2D Box Classification
Face
License: Unknown

Overview

IMDb-Face is a new large-scale noise-controlled dataset for face recognition research.
The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from
2.0 million raw images. All images are obtained from the IMDb website. A detailed introduction
of IMDb-Face can be found in the paper click here.

We hope that the
IMDb-Face dataset could shed lights on the influences of data noise to the face recognition
task, and point to potential labelling strategies to mitigate some of the problems. It could
serve as a relatively clean data to facilitate future studies of noises in large-scale face
recognition.

Citation

If you find IMDb-Face useful in your research, please cite:

@article{wang2018devil,
title={The Devil of Face Recognition is in the Noise},
author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian,
Chen and Loy, Chen Change},
journal={arXiv preprint arXiv:1807.11649},
year={2018}
}
0
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