FGAN: Fan-Shaped GAN for Racial Transformation

Racial bias in face recognition has recently been concerned by both general public and research community. Most face recognition systems have a strong bias in recognition accuracy for different races mainly because of the unbalanced ethnic distribution in their datasets. In this paper, we propose a novel generative adversarial network, which transfer the facial images of one race to corresponding images of other races, to facilitate the data augmentation to balance the ethnic distribution. Our approach can generate more realistic results and make the training process more stable than other image-to-image translation methods such as StarGAN and CycleGAN. Experiments results show the superiority of FGAN to the previous methods on the racial transformation task in terms of visual effects and quantitative results. Besides, we perform extensive experiments to show our data augmentation is beneficial to reduce the racial bias, improving the face recognition rate of non-Caucasian people. Finally, we show the possibility to generate the ethnic independent facial image by the average of various races.