Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation

TL;DR

This paper introduces a novel face aging simulation method using hidden factor analysis and sparse representation to generate natural aged face images while preserving identity, demonstrated on multiple databases.

Contribution

It models age-specific and person-specific facial features separately and transforms only the age component for realistic aging effects, a novel approach in face aging simulation.

Findings

Effective aging effect generation demonstrated on three databases.

High identity preservation in aged face images.

Robustness of the method across different datasets.

Abstract

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.