  Age synthesis is a challenging task due to the complicated and non-linear
transformation in human aging process. Aging information is usually reflected
in local facial parts, such as wrinkles at the eye corners. However, these
local facial parts contribute less in previous GAN based methods for age
synthesis. To address this issue, we propose a Wavelet-domain Global and Local
Consistent Age Generative Adversarial Network (WaveletGLCA-GAN), in which one
global specific network and three local specific networks are integrated
together to capture both global topology information and local texture details
of human faces. Different from the most existing methods that modeling age
synthesis in image-domain, we adopt wavelet transform to depict the textual
information in frequency-domain. %Moreover, to achieve accurate age generation
under the premise of preserving the identity information, age estimation
network and face verification network are employed. Moreover, five types of
losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2)
identity preserving loss aims to better preserve identity information; 3) age
preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise
loss aims to preserve the background information of the input face; 5) the
total variation regularization aims to remove ghosting artifacts. Our method is
evaluated on three face aging datasets, including CACD2000, Morph and FG-NET.
Qualitative and quantitative experiments show the superiority of the proposed
method over other state-of-the-arts.
