Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation


Face aging simulation has received rising investigations today, whereas it still remains a challenge to get convincing and natural age-progressed face images. In this paper, we gift a completely unique approach to such a problem using hidden issue analysis joint sparse illustration. In contrast to the bulk of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that have a tendency to be stable in a comparatively long period and the age-specific clues that delicately change over time. It then transforms the age part to a target age group via sparse reconstruction, yielding aging effects, that is finally combined with the identity part to attain the aged face. Experiments are administrated on three face aging databases, and therefore the results achieved clearly demonstrate the effectiveness and robustness of the proposed methodology in rendering a face with aging effects. Still, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

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