Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
This addresses the challenge of generating convincing age-progressed face images for applications like forensics or entertainment, but it is incremental as it builds on existing sparse representation methods.
The paper tackles face aging simulation by separating person-specific and age-specific facial components, using hidden factor analysis joint sparse representation to transform the age component, and demonstrates effectiveness on three aging databases with evaluations on identity preservation and aging effect generation.
Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue by using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that handle the facial texture integrally, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that change gradually over time. It then merely transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. Additionally, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.