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Computer scientists at the University of Washington have developed a piece of software that can automatically generate images of a young child's face as it would age through a lifetime -- from a single photo.

Baby faces tend to change drastically over their lifetime, making it hard to predict what they'll look like when they grow up.


The software developed by the researchers takes the average of thousands of faces of the same age and gender -- by looking at the average pixel arrangement from thousands of randomly selected web pictures -- and then calculates the changes between groups as they age, applying those changes to any new image. This makes it possible to see what a child will look like at any age up to 80.

University of Washington

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Ageing photographs of faces is a curious science, singe the ageing process itself is non-deterministic and dependent on environmental and genetic factors that may not be evident in the input photos. Also, appearance and recognisability is strongly influenced by hair style, glasses, expression and lighting. Despite this, age progression techniques have been quite helpful in solving missing children cases, but usually involve forensic artists who simulate the appearance of a person later in life. There are also fun apps such as AgingBooth, which will age anyone over 15, but these require frontal, simply-lit faces with a neutral expression.

This automated process -- developed by assistant professor Ira Kemelmacher-Shlizerman and colleagues -- allows you to take any image from any angle, with any expression and pose and apply algorithms to age the face, accounting the angle and light source.


It takes around 30 seconds to generate an older face using a standard PC.

To check the efficacy of the system, the team fed in child images of individuals for whom they also had adolescent and adult images. This allowed them to see how effective the software was at accurately ageing the children. This was put to the test by showing three pictures to human subjects (through Amazon's Mechanical Turk).

One picture would be an individual as a baby, and the two additional photos would be that person at a specific age (say 25) -- one generated by the software and one actual image of that person at that age. The volunteers had to say which of the two older photos were more like the baby.

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The results seem to show that humans identified the generated image as the older version almost as often as they identified the actual older image. 37 percent (out of 8,916 votes) said that the University of Washington team's approach was more likely to be the older baby, 44 percent saying the actual image was more likely.


15 percent of people said that both were equally likely to be the adult version of the baby, while five percent said neither were likely.

The team was surprised by this and decided to test whether humans are simply bad at recognising adults from their baby photos.

It did this using another database of images of the same people documented at different ages. The team showed volunteers two images of the same person separated by at least five years and asked if it was the same person or not. The results showed that people are good at recognising adults at different age ranges but poor at recognising children after many years.

The team also tried mapping the generated images onto the "ground truth" adult picture, taking into account the pose and lighting. This allowed for side-by-side comparison of the results.

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The team's results were also placed alongside the results from other ageing techniques, including Faceresearch PsychMorph and a technique developed by David Ian Perrett from the University of St Andrews.

The results seemed to show that for ageing young children, the University of Washington's technique outperformed all prior work.

The study authors say that future improvements for the work include: modelling wrinkles and hair whitening to enhance the realism of older subjects, increasing the range of ethnicities, and having a database of heads and upper torsos of different ages in order to apply the same technique to.


Kemelmacher-Shlizerman told Wired.co.uk that the biggest challenge was coming up with a method for "completely automatic analysis of face photos 'in the wild'", i.e. with unknown lighting, viewpoint and expression. "We've invented a method for "lighting-aware flow estimation" between such photos, and this opened up a huge amount of applications, the key in which is to use "big visual data" for novel face modelling and synthesis.

Co-author Steven Seitz said in a statement: "Our extensive user studies demonstrated age progression results that are so convincing that people can't distinguish them from reality. 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."

The work -- which was funded by Google and Intel -- is to be presented at the IEEE Computer Vision and Pattern Recognition conference in June.