  In this paper, we propose a method, called GridFace, to reduce facial
geometric variations and improve the recognition performance. Our method
rectifies the face by local homography transformations, which are estimated by
a face rectification network. To encourage the image generation with canonical
views, we apply a regularization based on the natural face distribution. We
learn the rectification network and recognition network in an end-to-end
manner. Extensive experiments show our method greatly reduces geometric
variations, and gains significant improvements in unconstrained face
recognition scenarios.
