Kinship-Guided Age Progression

Age progression is defined as aesthetically re-rendering an aging face with identity preservation and high credibility at any future age for an input face. There are two main challenges in age progression: (1) age progression of a specific individual is stochastic and non-deterministic, though there exist some general changes and resemblances in this process for a relatively large population; (2) there may not be apparent identity information for people at the tender age. In this work, we present an efficient and effective Kinship-Guided Age Progression (KinGAP) approach for an individual, which can automatically generate personalized aging images by leveraging kinship, or more specifically, with guidance of the senior kinship face. The proposed approach mainly consists of three aging modules, which are designed to preserve individual aging characteristics, capture human aging tendency, and guide aging direction, respectively. Extensive experimental results and user study analysis on our constructed age-kinship face dataset validate the superiority of our approach. HighlightsWe aim to improve the performance of age progression by leveraging available kinship information.We consider both the global aging direction and the individual-specific aging diversity.We present an efficient and effective KinGAP approach.The presented KinGAP approach mainly consists of three aging modules.Extensive experimental results validate the superiority of our approach.