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X-rays use invisible electromagnetic energy beams to make images of internal tissues, bones, and organs on film. Standard X-rays are performed for many reasons. These include diagnosing tumors or bone injuries. X-rays are made by using external radiation to produce images of the body, its organs, and other internal structures for diagnostic purposes. Instead of film, X-rays are now typically made by using computers and digital media.

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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.

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? 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.

This kind of facial recognition work emerges from very different places: forensic scientists, pure computer scientists, and facial recognition practitioners. 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.

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. Elusive, perhaps, but not impossible. 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.

For Soatto, a face is shape-space with certain properties. For him, the problem is that if we want to match photos or discriminate between individuals, there are two kinds of variability.

Soatto wrote a paper on getting rid of this kind of variability with respect to focal length in images, which heavily distort people's faces especially the front-facing one on your phone: "you cannot see your ears, your nose looks bigger". And he saw a parallel between the mathematics underlying that research and quantifying the effects of aging.

He believes that using the same methodology as in the focal length study could be applied to aging and facial recognition. They could feed lots of images into the model and "learn away the variability," he said. The only difficulty is getting the data for this. You need consent and it would be a long, longitudinal study. The challenge, of course, is that we all age differently, but "there is geometric consistency because faces are not arbitrary objects.

Or for children, one can expect the size of the forehead to even out relative to the rest of the face. Perhaps some set of the dozens of possible landmarks on a face hold constant, or the relationships between them do. To keep things simple, assume they are portraits frontal pose, neutral expression and uniform illumination. And, Jain noted, that face matching in "unconstrained settings" like surveillance video or random snapshots "poses challenges.

What happens when one attains Facebook scale, billions of photos, with thousands or even tens of thousands of images of an individual through time? You can bet that Facebook is going to try to identify its users no matter how old they are, or in whose pictures they appear.

And they'll probably get good, too, as the " unreasonable effectiveness of data " makes their algorithms better. Soatto, for one, doubts it. The problem is that it goes to "the core of what knowledge and learning is," Soatto said. Information is what is left in the data after you throw away what doesn't matter to your task. You've picked one source of nuisance variability, which is age, and that's a tough one.

But that same conundrum permeates every other branch of knowledge and learning. The fundamental nature of the problem might suggest that until the machines can learn as well as we do, it will be difficult for them to overtake us in facial recognition tasks. We want to hear what you think about this article.

Submit a letter to the editor or write to letters theatlantic. Will computers ever get better than humans? Alexis C. Twitter Email.

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X-rays of the Spine, Neck or Back | Johns Hopkins Medicine

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