  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.
