Feature-Improving Generative Adversarial Network for Face Frontalization

Face frontalization can boost the performance of face recognition methods and has made significant progress with the development of Generative Adversarial Networks (GANs). However, many GAN-based face frontalization methods still perform relatively weak on face recognition tasks under large face poses. In this paper, we propose Feature-Improving GAN (FI-GAN) for face frontalization, which aims to improve the recognition performance under large face poses. We assume that there is an inherent mapping between the frontal face and profile face, and their discrepancy in deep representation space can be estimated. The generation module of FI-GAN has a compact module named Feature-Mapping Block that helps to map the features of profile face images to the frontal space. Moreover, we produce a feature discriminator that can distinguish the features of profile face images from those of ground true frontal face images, which guide the generation module to provide high-quality features of profile faces. We conduct experiments on the MultiPIE, Labeled Faces in the Wild (LFW), and Celebrities in Frontal-Profile (CFP) databases. Our method is comparable to state-of-the-art methods under small poses and outperforms them on large pose face recognition.