Large Pose Face Recognition via Facial Representation Learning
Jingwei Xin, Zikai Wei, Nannan Wang, Jie Li, Xinbo Gao
Abstract
Overcoming image acquisition perspectives and face pose variations is a key problem in unconstrained face recognition tasks. One of the practical approaches is by reconstructing the face with extreme pose into a version that is more easily recognized by the discriminator, such as a frontal face. Often, existing methods attempt to balance the accuracy of downstream tasks with human visual perception, but ignore the differences in propensity between the two. Besides, large-scale datasets of profile-frontal paired face images are absent, which further hinders the training of models. In this work, we investigate a variety of face reconstruction approaches and propose a very simple, but very effective method to match face images across different scenes, named facial representation learning (FRL). The core idea of FRL is to introduce a representation generator in front of a pre-trained face recognition model, which can extract face representations from arbitrary faces that are more suitable for recognition model discrimination. In particular, the representation generator reconstructs the facial representation by minimising identity differences from the frontal face and adds pixel-level and adversarial constraints to cater for discriminator preferences. Extensive benchmark experiments show that the proposed method not only achieves better performance than state-of-the-art methods, but also can further squeeze the inference potential of existing face recognition models.