README.md

metadata

licenses:
  - cc-by-4.0
size_categories:
  - 10K<n<100K
task_categories:
  - other
task_ids:
  - other-image-classification
  - image-classification
paperswithcodeid: fairface-face-attribute-dataset-for-balanced

Dataset Card for FairFace

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: FairFace repository
• Paper: FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age

Dataset Summary

Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. This can lead to inconsistent model accuracy, limit the applicability of face analytic systems to non-White race groups, and adversely affect research findings based on such skewed data. To mitigate the race bias in these datasets, we construct a novel face image dataset, containing 108,501 images, with an emphasis of balanced race composition in the dataset. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle East, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure generalization performance. We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent between race and gender groups.

Data Fields

• img_bytes: Bytes representing an image
• age: Age of the person in the image
• gender: Gender of the person in the image
• race: Race of the person in the image

Data Instances

{
  'age': 6,
  'gender': 1,
  'img_bytes': b'\xff\xd8...',
  'race': 1
}