Computers See Your Face as a Child: Will They Recognize You as an Adult?

As facial recognition systems improve, they will get better at identifying people at different ages, even very young children.

This story began with a simple question: if a facial recognition system processes a lot of pictures of a child, will it recognize that person when he or she grows up?

If I were to upload all my childhood photos to Facebook (or some future Facebook), could a biometric identification system link the button-nosed, round-cheeked child with a bowl cut to my adult face, which has lost its button, cheeks, and hair? 

It's not an idle question: parents are posting millions of photos of their children to social networking sites, as are kids themselves when they are old enough to use Facebook and the like. Will these photos permanently identify them as they grow older, linking their childhood or teenage antics to their adult identities? 

Or does the natural aging process provide some level of protection from the prying computations of facial recognition algorithms? If I can barely identify myself in photos from my childhood, what hope does a computer have?

There is no simple answer, though there is a good theoretical lower age-limit: it would be very difficult for a facial recognition system to match up a photograph of a child under the age of seven with a photograph of that same person as an adult.

And, in practice, most facial recognition systems aren't close to being able to do this kind of identification in the field. Still, that might not allay the worries of technology thinkers like Amy Webb, who recently warned parents to post no photographs of their children online because "ubiquitous bio-identification is only just getting started."

What's at stake is this: how firmly do we want the media that children produce to attach to their adult identities? For most current adults, the pictures and videos we made as kids are not searchable or accessible, except for the hand-curated selections of "throwback Thursday."

Kids now are growing up on the Internet are trailed by an ever larger and deeper digital footprint. The danger is that it might restrict their freedom to develop as future people. Algorithms rely on what they know about someone's childhood to channel their possibilities as an adult.

If pictures (or YouTube videos) from your youth can be connected to your adult identity, it would, at the very least, increase the ethical complexity of posting or hosting images of children. 

Let's get into the details.

This kind of facial recognition work emerges from very different places: forensic scientists, pure computer scientists, and facial recognition practitioners. Forensic scientists are trying to solve a very practical problem: if a child goes missing for some long period of time, how can law enforcement create a more up-to-date portrait of the child? They want a system that can artificially age a missing child's face. We all know kids change, but that's not the kind of rigorous analysis one needs to Photoshop five years onto a child's visage. Artificial aging is almost the reverse of what a facial recognition system would do. 

The fastest changes are between infancy to 3, and then during adolescence (after 10 years old) into adulthood, said Alex Cybulski, a doctoral candidate at the University of Toronto Information School, where he's studying surveillance. "You can understand how this complicates things as the changes to the craniofacial shape and texture of a face during the early period of an individual's life are rapid and thus elusive to estimation by computer modeling for the purposes of facial recognition."

Elusive, perhaps, but not impossible. Cybulski pointed to the work of forensic researcher Stuart Gibson at the University of Kent, who "has proposed that because of the way in which the face changes during [childhood] starting at age seven is considered the maximum range through which changes can be estimated and therefore, reliably compared."

What Gibson has done is try to take images of children at various ages and build computer models that attempt to artificially age them. So, for example, here, the photos on the far left (A) and right (F) are actual pictures of the subjects. Columns B through E show different algorithmic projections based on his models. 

One can imagine that these attempts to quantitatively model the changes in bone structure, skin texture, and other aesthetic variables might lead to a better facial recognition system. 

Another place this quixotic question led me was to mathematicians like Nigel Boston at the University of Wisconsin, Madison. He referred me to the work of UCLA's Stephen Soatto. 

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Alexis C. Madrigal

Alexis Madrigal is the deputy editor of He's the author of Powering the Dream: The History and Promise of Green Technology. More

The New York Observer has called Madrigal "for all intents and purposes, the perfect modern reporter." He co-founded Longshot magazine, a high-speed media experiment that garnered attention from The New York Times, The Wall Street Journal, and the BBC. While at, he built Wired Science into one of the most popular blogs in the world. The site was nominated for best magazine blog by the MPA and best science website in the 2009 Webby Awards. He also co-founded Haiti ReWired, a groundbreaking community dedicated to the discussion of technology, infrastructure, and the future of Haiti.

He's spoken at Stanford, CalTech, Berkeley, SXSW, E3, and the National Renewable Energy Laboratory, and his writing was anthologized in Best Technology Writing 2010 (Yale University Press).

Madrigal is a visiting scholar at the University of California at Berkeley's Office for the History of Science and Technology. Born in Mexico City, he grew up in the exurbs north of Portland, Oregon, and now lives in Oakland.

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