Face aging, which renders aging faces for an input face, has attracted
extensive attention in the multimedia research. Recently, several conditional
Generative Adversarial Nets (GANs) based methods have achieved great success.
They can generate images fitting the real face distributions conditioned on
each individual age group. However, these methods fail to capture the
transition patterns, e.g., the gradual shape and texture changes between
adjacent age groups. In this paper, we propose a novel Contextual Generative
Adversarial Nets (C-GANs) to specifically take it into consideration. The
C-GANs consists of a conditional transformation network and two discriminative
networks. The conditional transformation network imitates the aging procedure
with several specially designed residual blocks. The age discriminative network
guides the synthesized face to fit the real conditional distribution. The
transition pattern discriminative network is novel, aiming to distinguish the
real transition patterns with the fake ones. It serves as an extra
regularization term for the conditional transformation network, ensuring the
generated image pairs to fit the corresponding real transition pattern
distribution. Experimental results demonstrate the proposed framework produces
appealing results by comparing with the state-of-the-art and ground truth. We
also observe performance gain for cross-age face verification.

本文提出一种新颖的 C-GAN 模型，用于生成更贴合真实数据的老化人脸图像，并且该模型能够考虑到相邻年龄组之间的渐变变化，从而提高了跨年龄人脸验证的性能。