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A New Model to Simulate Human Facial Appearance, Accurately, and Realistically, across Age and Ethnicity

Background: The human population is aging globally, and there is significant, growing interest in modeling and simulating facial appearance. Methods: The authors describe a new means to model and simulate aging in facial images, using an approach based entirely on 3D whole-face data collected from 1250 female subjects, across 5 ethnicities, ages 10–80. Results: Three models were built, each describing changes with age within each ethnic group, namely shape, color, and topography. These three models were used to build a simulation able to age or de-age a 2D image of a female subject’s face, with a degree of accuracy and realism not achievable with previous approaches. Simulated images were validated by a cloud-based age estimator. Conclusions: The authors have developed a new facial age simulation model, where the use of three submodels (shape, color and topography), built from acquired 3D data, provides both scientifically robust and realistic output. As the data were acquired across five of the world’s major ethnicities, this new model allows valuable insight into changes in the facial appearance of our aging global population.