Facial Age Progression using Conditional Generative Adversarial Network with Heritable Visual Features

Abstract

Age progression of face images has been an important tool to search for missing children. Many studies on age progression were recently conducted by conditional Generative Adversarial Networks (cGAN) based methods. However, these methods cannot estimate facial aging from a child’s face in the early childhood stage, which exhibits drastic facial shape changes over time. Thus, the problem of age progression from young children remains challenging in the field. In this study, we propose a cGAN based two-stage age progression model considering heritable facial features from parents and child to generate candidates for age-progressed face images from a young child’s face image.