Effective face frontalization in unconstrained images

@article{Hassner2015EffectiveFF,
  title={Effective face frontalization in unconstrained images},
  author={Tal Hassner and Shai Harel and Eran Paz and Roee Enbar},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={4295-4304}
}
  • Tal HassnerShai Harel Roee Enbar
  • Published 28 November 2014
  • Computer Science
  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
“Frontalization” is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. [] Key Result More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.

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A new and effective frontalization algorithm for frontal rendering of unconstrained face images, and results comparable to state-of-the-art on two challenging benchmark datasets are reported, supporting the claim of effectiveness of the proposed face image representation.

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A novel method, robust to pose, illumination variations, and occlusions is proposed for joint face frontalization and landmark localization and the experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.

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This work proposes a method for face frontalization based on the use of 3D models obtained from 2D images that allows to effectively align the images in an efficient way and compares it with other frontalization techniques using different face recognition methods.

Face Synthesis from Facial Identity Features

We present a method for synthesizing a frontal, neutralexpression image of a person’s face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from
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