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.
CITATION STYLE
Zhou, E., Cao, Z., & Sun, J. (2018). GridFace: Face Rectification via Learning Local Homography Transformations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11220 LNCS, pp. 3–20). Springer Verlag. https://doi.org/10.1007/978-3-030-01270-0_1
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