LDF-Net: Learning a Displacement Field Network for Face Recognition across Pose
Face recognition is an important problem in computer vision, however, it is still challenging due to a few wild factors, such as large variations caused by pose, expression, lighting, etc. In this work, we mainly focus on dealing with the pose variations for face recognition. The proposed method attempts to directly transform a non-frontal face image into frontal one by Learning a Displacement Field network (LDFNet) and then recognizes with the transformed images. The existing methods, that follow the same scheme of transforming non-frontal faces into frontal ones, either transform by using 3D-model (3D methods) or transform by using 2D reconstructive methods (2D methods). The 3D methods may lead to the invisibility of some pixels in the transformed frontal images, while the 2D methods may lead to difference between the pixels in the transformed frontal images and the original non-frontal images. Our proposed LDF-Net method can handle these two problems by learning a morphable displacement field for each pixel in the transformed frontal image. Therefore, LDF-Net can achieve a frontal image where all pixels are from the original non-frontal image pixels and no invisible pixels exist, so as to maintain the informative information from the non-frontal images as much as possible. The experiments on MultiPIE dataset show that the proposed LDF-Net achieves state-of-theart performance for face recognition across pose, especially for those large poses.