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-theart face recognition and age estimation solutions demonstrate the high potential of the proposed method.
Face aging with conditional generative adversarial networks
ICIP 2017, IEEE International Conference on Image Processing, 17-20 September 2017, Beijing, China / Also on ArXiv
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