ChildGAN: Large Scale Synthetic Child Facial Data Using Domain
  Adaptation in StyleGAN
Muhammad Ali Farooq Wang Yao Gabriel Costache Peter Corcoran
In this research work, we proposed a novel ChildGAN, a pair of GAN networks
for generating synthetic boys and girls facial data derived from StyleGAN2.
ChildGAN is built by performing smooth domain transfer using transfer learning.
It provides photo-realistic, high-quality data samples. A large-scale dataset
is rendered with a variety of smart facial transformations: facial expressions,
age progression, eye blink effects, head pose, skin and hair color variations,
and variable lighting conditions. The dataset comprises more than 300k distinct
data samples. Further, the uniqueness and characteristics of the rendered
facial features are validated by running different computer vision application
tests which include CNN-based child gender classifier, face localization and
facial landmarks detection test, identity similarity evaluation using ArcFace,
and lastly running eye detection and eye aspect ratio tests. The results
demonstrate that synthetic child facial data of high quality offers an
alternative to the cost and complexity of collecting a large-scale dataset from
real children.