  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.
