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.
@article{arxiv.1702.01983,
title = {Face Aging With Conditional Generative Adversarial Networks},
author = {Grigory Antipov and Moez Baccouche and Jean-Luc Dugelay},
journal= {arXiv preprint arXiv:1702.01983},
year = {2017}
}
Comments
5 pages, 3 figures, accepted at ICIP 2017. With respect to v1: (1) changed the abbreviation of the main model from "acGAN" to "Age-cGAN" in order to avoid confusion with "Auxiliary Classifier Generative Adversarial Networks" introduced by Odena et al.; (2) corrected a typo in Formula 1