Pose-Weighted Gan for Photorealistic Face Frontalization

Face recognition methods have achieved high accuracy when faces are captured in frontal pose and constrained scenes. However, severe drop in accuracy is observed when large pose variations exist. The main reason is that the large yaw angle leads to ID information loss. In this paper, we intend to solve the large pose variations in a generation manner. Specifically, we propose a Pose-Weighted Generative Adversarial Network (PW-GAN) for photorealistic frontal view synthesis. We find frontalizing the faces in large poses (yaw angle larger than 60◦) is so difficult that the results are not photorealistic and the ID information is lost. To simplify the problem, we first frontalize the face image through 3D face model, which is then used to guide the network predicting. Second, we refine the pose code in the loss function to make the network pay more attention to large poses. Quantitative and qualitative experimental results on the Multi-PIE and LFW demonstrate our method achieves state of the art.