Digital face aging with neural network

Disney Research demonstrates their use of neural networks to seamlessly age and de-age someone’s face across a continuous range. Sure what is real anymore anyway.

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Noah Kalina’s averaged face over 7,777 days

Noah Kalina has been taking a picture of himself every day since January 11, 2000. He posted time-lapse videos in 2007, 2012, and 2020. Last year was the 20th of the project.

Usually Kalina’s videos are a straight up time-lapse using every photo. But in this collaboration with Michael Notter, 7,777 Days shows a smoother passage of time. Notter used machine learning to align the face pictures, and then each frame shows a 60-day average, which focuses on an aging face instead of everything else in the background.

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Fake faces created by AI and where this might be headed

It’s grown easier and easier to generate fake faces with AI. For The New York Times, Kashmir Hill and Jeremy White demonstrate the tech with a slick interactive. Quickly adjust age, eye, mood, and gender. All fake.

It was only a few years ago when the idea seemed novel. One year later, there were guides (and warnings) for spotting fake faces. By 2019, there was a marketplace for fake faces (of course). Sometimes it’s scary to think about what the internet will be in five years.

In any case, check out the NYT piece. The smooth transitions between faces, one facial aspect at a time, is mesmerizing.

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Face depixelizer with machine learning, and some assumptions

In crime shows, they often have this amazing tool that turns a low-resolution, pixelated image of a person’s face to a high-resolution, highly accurate picture of the perp. Face Depixelizer is a step towards that with machine learning — except it seems to assume that everyone looks the same.

There might still be some limitations.

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Machine learning to help you not touch your face

The CDC recommends that you do not touch your face to minimize the spread of the coronavirus. We do this quite a bit without even thinking about it, so Do Not Touch Your Face uses machine learning to help you adjust. Train the algorithm, and then the algorithm trains you.

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Reconstructing faces from brain waves

Researchers attached electrodes to neurons in monkeys, showed them pictures of faces, and then reconstructed the faces reading brain waves.

After decades of work, scientists at Caltech may have finally cracked our brain’s facial recognition code. Using brain scans and direct neuron recording from macaque monkeys, the team found specialized “face patches” that respond to specific combinations of facial features.

Like dials on a music mixer, each patch is fine-tuned to a particular set of visual information, which then channel together in different combinations to form a holistic representation of every distinctive face.

There are so many ways this could be used irresponsibly, but to be honest, tech-enhanced photographic memory sounds kind of awesome.

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DNA face estimation

DNA faceParabon NanoLabs is working on a service that provides face estimates using DNA found at crime scenes. Pretty cool.

But, before anyone gets too excited, keep in mind that the estimates are still really rough.

Greytak agrees Snapshot is not super-precise, nor is the science ready for it to be. "Our goal is not to produce a profile that is perfectly accurate and there is only one person you've ever seen who could match that profile," she says. "Really our goal is to produce something that will look similar enough to a person that it will jog a memory and, at the same time, make it clear which people it is not."

There was an art project a few years ago that was something like this. The artist used DNA in gum and hair that she found in public spaces, and I could've sworn I shared it. Anyone know what I'm talking about? Update: It was Stranger Visions. (Thanks, Thomas.)

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Posted by in dna, face, statistics

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