IE-WGAN: A model with Identity Extracting for Face Frontal Synthesis

Abstract

Face pose affects the effect of the frontal face synthesis, we propose a model used for frontal face synthesis based on WGAN-GP. This model includes identity extracting module, which is used to supervise the training of the face generation module. On the one hand, the model improves the quality of synthetic face images, on the other hand, it can accelerate the convergence speed of network training. We conduct verification experiments on CelebA data sets, and the results show that this model improves the graphic quality of frontal synthesis.