  As the social impact of visual recognition has been under scrutiny, several
protected-attribute balanced datasets emerged to address dataset bias in
imbalanced datasets. However, in facial attribute classification, dataset bias
stems from both protected attribute level and facial attribute level, which
makes it challenging to construct a multi-attribute-level balanced real
dataset. To bridge the gap, we propose an effective pipeline to generate
high-quality and sufficient facial images with desired facial attributes and
supplement the original dataset to be a balanced dataset at both levels, which
theoretically satisfies several fairness criteria. The effectiveness of our
method is verified on sex classification and facial attribute classification by
yielding comparable task performance as the original dataset and further
improving fairness in a comprehensive fairness evaluation with a wide range of
metrics. Furthermore, our method outperforms both resampling and balanced
dataset construction to address dataset bias, and debiasing models to address
task bias.
