[1702.01983] Face Aging With Conditional Generative Adversarial Networks

Face Aging With Conditional Generative Adversarial Networks  #DeepLearning

  • In the work, we propose the GAN-based method for automatic face aging.
  • Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person’s identity in the aged version of his/her face.
  • Abstract: It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.
  • The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.
  • We introduce a novel approach for “Identity-Preserving” optimization of GAN’s latent vectors.


@alexjc: Face Aging With Conditional Generative Adversarial Networks #DeepLearning

Abstract: It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person’s identity in the aged version of his/her face. To this end, we introduce a novel approach for “Identity-Preserving” optimization of GAN’s latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.

Comments: 5 pages, 3 figures, submitted to ICIP 2017

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Link back to: arXiv, form interface, contact.

[1702.01983] Face Aging With Conditional Generative Adversarial Networks