Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning

Understand

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly.

• More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians.
• To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses.
• A reinforcement learning based race balance network (RL-RBN) is proposed.