Abstract Represented in a Morphable Model, 3D faces follow curved trajectories in face space as they age. We present a novel algorithm that computes the individual aging trajectories for given faces, based on a non‐linear function that assigns an age to each face vector. This function is learned from a database of 3D scans of teenagers and adults using support vector regression. To apply the aging prediction to images of faces, we reconstruct a 3D model from the input image, apply the aging transformation on both shape and texture, and then render the face back into the same image or into images of other individuals at the appropriate ages, for example images of older children. Among other applications, our system can help to find missing children.
Scherbaum et al. (Sat,) studied this question.