Abstract
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.
Abstract (translated)
在本研究中,我们提出了一种新的ChildGAN,即从StyleGAN2中提取合成男孩和女孩面部数据的GAN网络,ChildGAN通过使用转移学习实现平滑域转换,提供逼真高质量的数据样本。它提供了多种智能面部变换,包括面部表情、年龄进展、眨眼效应、头部姿势、皮肤和头发颜色的变化,以及多种照明条件。该数据集包含超过300k个不同的数据样本。此外,通过运行不同的计算机视觉应用程序测试,包括基于卷积神经网络的儿童性别分类器、面部定位和面部地标检测测试、使用ArcFace进行身份相似度评估,最后运行眼检测和眼 aspect ratio测试,结果验证渲染面部特征的独特性和特征,证明高质量的合成儿童面部数据可以替代从真实儿童收集大规模数据的成本和复杂性。
URL
https://arxiv.org/abs/2307.13746
