The researchers tested their rendered images against those of 82 actual people photographed over a span of years. These changes are then applied to a new child’s photo to predict how she or he will appear for any subsequent age up to 80. An algorithm then finds correspondences between the averages from each bracket and calculates the average change in facial shape and appearance between ages. More specifically, the software determines the average pixel arrangement from thousands of random Internet photos of faces in different age and gender brackets. This technique leverages the average of thousands of faces of the same age and gender, then calculates the visual changes between groups as they age to apply those changes to a new person’s face. The shape and appearance of a baby’s face – and variety of expressions – often change drastically by adulthood, making it hard to model and predict that change. See more examples of age-progressed photos.
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