The large variations of pose and illumination have been the great challenges to face recognition for many years. Because of these variations, many classical recognition methods fail to work. The key to solve this problem is to extract identity feature from face images. In recent years, people have been concentrating on synthesizing rotated faces, however, neglected the form of facial identity representation. In this paper, we propose Cross-generating Generative Adversarial Network (CG-GAN) to generate rotated faces while extracting discriminative identity. CG-GAN is allowed to learn a network to exchange poses and illuminations of two different subjects' picture. Within the network, each input image is resolved into a variation code and a identity code at the representation layer; then these codes are randomly combined for generating corresponding pictures. Not only does CG-GAN synthesis vivid face under desired pose from one picture, but also the represention layer is very suitable for face recognition task. We train and test CG-GAN on the Multi-PIE dataset and achieve state-of-the-art results.
