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GridFace: Face Rectification via Learning Local Homography Transformations

  • Erjin Zhou
  • Zhimin Cao
  • Jian Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

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.

Keywords

Face recognition Face rectification Homography transformation 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Face++, Megvii Inc.BeijingChina

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