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.

使用StyleGAN2和转移学习构建的ChildGAN，通过平滑的领域转换生成高质量的、逼真的合成男孩和女孩面部数据，这些数据的独特性和特征已经通过不同的计算机视觉应用测试得到验证，结果表明高质量的合成儿童面部数据为替代实际儿童大规模数据的成本和复杂性提供了一个选择。

ChildGAN: 基于风格适应的大规模合成儿童面部数据