Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy
This addresses data scarcity for face recognition systems, though it is incremental as it builds on existing GAN methods.
The paper tackles the problem of limited face recognition training data by proposing a novel GAN to generate photo-realistic synthetic images, which increases face recognition accuracy compared to using real images alone.
In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete identity labels to an identity latent space that follows a simple prior distribution, and training a GAN conditioned on samples from that distribution. Our proposed GAN allows us to augment face datasets by generating both synthetic images of subjects in the training set and synthetic images of new subjects not in the training set. By using recent advances in GAN training, we show that the synthetic images generated by our model are photo-realistic, and that training with augmented datasets can indeed increase the accuracy of face recognition models as compared with models trained with real images alone.