https://github.com/amazon-science/widerface-demographics

https://github.com/amazon-science/widerface-demographics

Science Score: 36.0%

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Repository

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  • Host: GitHub
  • Owner: amazon-science
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 3.47 MB
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Created over 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme Contributing License

README.md

Amazon Alexa WiderFace Demographics

Fairness has become an important agenda in computer vision and artificial intelligence. Demographically diverse datasets can help mitigate bias. We collected perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE. In this repository, we release the demographic annotations for a subset of Widerface data which can be used by works to retrospect on their model fairness, and to mitigate biases in face detection.

More information about our work can be found in https://dl.acm.org/doi/abs/10.1145/3514094.3534153

Dataset statistics

We annotated a subset of WiderFace data and added demographic attributes such as perceived age, perceived gender and perceived skintone

From the training split we annotated around 9k faces, and trained a demographic classifier using those images and generated psuedolabels for the remaining faces, totalling to aroung 150k faces, from 12k images

For Validation, We annotated 12k faces from 3200 images.

Since we do not have test bounding boxes, we only annotate training and validation data

The annotation file

The annotation file contains Image Filename | perceivedgender | perceivedage | perceived_skin

You can download the images and bbox annotations from the Widerface website - http://shuoyang1213.me/WIDERFACE/

Since we only annotate a subset of users, once downloaded, You can use the script provided to merge the demographic annotations with the widerface bounding box annotations

Citation

@Inproceedings{Yang2022, author = {Yu Yang and Aayush Gupta and Jianwei Feng and Prateek Singhal and Vivek Yadav and Yue Wu and Pradeep Natarajan and Varsha Hedau and Jungseock Joo}, title = {Enhancing fairness in face detection in computer vision systems by demographic bias mitigation}, year = {2022}, url = {https://www.amazon.science/publications/enhancing-fairness-in-face-detection-in-computer-vision-systems-by-demographic-bias-mitigation}, booktitle = {AIES 2022}, }

License Summary

The annotation data and code are made available under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See the LICENSE file.

Owner

  • Name: Amazon Science
  • Login: amazon-science
  • Kind: organization

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