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As the social impact of visual recognition has been under scrutiny, severalprotected-attribute balanced datasets emerged to address dataset bias inimbalanced datasets. However, in facial attribute classification, dataset biasstems from both protected attribute level and facial attribute level, whichmakes it challenging to construct a multi-attribute-level balanced realdataset. To bridge the gap, we propose an effective pipeline to generatehigh-quality and sufficient facial images with desired facial attributes andsupplement the original dataset to be a balanced dataset at both levels, whichtheoretically satisfies several fairness criteria. The effectiveness of ourmethod is verified on sex classification and facial attribute classification byyielding comparable task performance as the original dataset and furtherimproving fairness in a comprehensive fairness evaluation with a wide range ofmetrics. Furthermore, our method outperforms both resampling and balanceddataset construction to address dataset bias, and debiasing models to addresstask bias.

CAT: Controllable Attribute Translation for Fair Facial Attribute Classification