Learning Face Age Progression: A Pyramid Architecture of GANs

Abstract

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.

CVPR 2018. V4 and V2 are the same, i.e. the conference version; V3 is a related but different work, which is mistakenly submitted and will be submitted as a new arXiv paper

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Unsupervised Cross-Domain Image Generation
• Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

Cited by in corpus (5)

• Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
• Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression
• Global and Local Consistent Age Generative Adversarial Networks
• Mask-Guided Portrait Editing with Conditional GANs
• How Old Are You? Face Age Translation with Identity Preservation Using GANs