Progressive Pose Normalization Generative Adversarial Network for Frontal Face Synthesis and Face Recognition Under Large Pose

Abstract

This paper proposes a Progressive Pose-Normalization Generative Adversarial Network (PPN-GAN) for frontal face synthesis and face recognition. The key idea is to normalize a profile face progressively: starting from inferring an intermediate face that has a small view difference to the profile face, and then increasing the view difference step by step, until the frontal view of the profile face is recovered. In addition to the progressive strategy, an additional identity discriminator and identity-aware losses in both the image and feature spaces are also incorporated into the GAN for identity preserving. Experimental results show that our method not only produces compelling perceptual results but also outperforms the state-of-the-art methods on face recognition under large-pose.