Age-Oriented Face Synthesis with Conditional Discriminator Pool and Adversarial Triplet Loss

Abstract

The vanilla Generative Adversarial Networks (GAN) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may result in the generation of faces with minimal variations and a poor synthesis accuracy. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information on synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are associated with a trivial weighting factor. In this paper, we propose a method for the age-oriented face synthesis task that achieves a high synthesis accuracy with strong identity permanence capabilities. Specifically, to achieve a high synthesis accuracy, our method tackles the mode collapse issue with a novel Conditional Discriminator Pool (CDP), which consists of multiple discriminators, each targeting one particular age category. To achieve strong identity permanence capabilities, our method uses a novel Adversarial Triplet loss. This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding resulting in significantly reduced intra-class variances in the feature space. Through extensive experiments, we show that our proposed method outperforms state-of-the-art methods in terms of synthesis accuracy and identity permanence capabilities, qualitatively and quantitatively.

References in corpus (10)

• Auto-Encoding Variational Bayes
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Conditional Generative Adversarial Nets
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• In Defense of the Triplet Loss for Person Re-Identification
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• The relativistic discriminator: a key element missing from standard GAN
• Unrolled Generative Adversarial Networks
• Personalized Age Progression with Bi-level Aging Dictionary Learning
• Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step