Mitigating Face Recognition Bias via Group Adaptive Classifier

Understand

Face recognition is known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups.

• This work aims to learn a fair face representation, where faces of every group could be more equally represented.
• Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes.
• The adaptive module comprises kernel masks and channel-wise attention maps for each demographic group so as to activate different facial regions for identification, leading to more discriminative features pertinent to their demographics.