Face Aging With Conditional Generative Adversarial Networks

2017/02/07 by Antipov, Grigory, Baccouche, Moez, Dugelay, Jean-Luc · 3 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1702.01983

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.

Cited by

• AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models
• A Race Bias Free Face Aging Model for Reliable Kinship Verification
• Identity-Preserving Aging and De-Aging of Faces in the StyleGAN Latent Space

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