FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age

TL;DR

FairFace introduces a balanced face dataset with diverse race, gender, and age groups to improve fairness and accuracy in face analysis models across different demographic groups.

Contribution

The paper presents a new, balanced face dataset with 108,501 images across seven race groups, addressing bias in existing datasets and enhancing model fairness.

Findings

Models trained on FairFace are more accurate on diverse datasets.

FairFace reduces racial and gender bias in face attribute recognition.

Improved generalization performance across demographic groups.

Abstract

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.