It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the first GANbased 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.
al REF have recently shown that GANs can be implemented to produce images for automatic face aging via the "Identity-Preserving" optimization of GAN's latent vectors to incorporate face recognition and age estimation solutions.
Face aging with conditional generative adversarial networks
2017 IEEE International Conference on Image Processing (ICIP)
2017 IEEE International Conference on Image Processing (ICIP)
Computer Science