“When shown images of an age-progressed child photo and a photo of the same person as an adult, people are unable to reliably identify which one is the real photo.” “Our extensive user studies demonstrated age progression results that are so convincing that people can’t distinguish them from reality,” said co-author Steven Seitz, a UW professor of computer science and engineering. In an experiment asking random users to identify the correct aged photo for each example, they found that users picked the automatically rendered photos about as often as the real-life ones.Ī single photo of a child (far left) is age progressed (left in each pair) and compared to actual photos of the same person at the corresponding age (right in each pair). 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. MORPH AGE PRO WINDOWS SOFTWAREMore 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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