Boosting Face Recognition Performance with Synthetic Data and Limited Real Data

Face recognition is one of the most precise and straightforward methods to establish individual identity, and is important in our daily life. To solve the issues of privacy, bias, and collection difficulty caused by face recognition relying heavily on collecting a huge number of real face images from the Internet, a seemingly promising idea is to employ GAN-generated synthetic faces as the training data. However, there are obvious surface gap and domain gap between real and synthetic face images, and cannot be replaced directly. In this paper, we attempt to boost face recognition simultaneously using synthetic data and limited real data. Specifically, we first design an augmented space for auto augmentation methods to augment synthetic images to alleviate the surface gap, then propose to disentangle the underlying style distributions through dual batch normalization layers so that both synthetic and real images can be learned jointly by convolution layers without mixing across domains. Extensive experiments demonstrate our method can achieve better results than training with large quantities of real data.