Abstract. 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.