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Face detection
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Breakthrough in facial recognition: the ‘Deep Dense Face Detector’

A breakthrough in facial recognition technology means that computers will soon be able to spot faces even if they're tilted or partially blocked: a boon to cloud storage providers, social networks, surveillance and advertisers alike.

Just take a look at all these great actors.

I’m no celebrity expert, but I can easily recognise Jennifer Lawrence, Ellen DeGeneres, Kevin Spacey, Brad Pitt, Bradley Cooper and a bunch of others whose names escape me:

Face detection

I can recognise them even though they’re not all looking directly at the camera, and in spite of the fact that many have their heads tilted to one side, while the faces of others – that’s Angelina Jolie, back there, right? – are partially obscured.

Such elements of facial recognition come naturally to us, as humans, but they’ve been challenging to state-of-the-art computer facial recognition – until now.

A breakthrough in facial recognition technology means that computers will soon be able to boast greatly improved success at detecting faces in images, in spite of those faces being tilted or partially blocked.

On Tuesday, Sachin Farfade and Mohammad Saberian at Yahoo Labs in California and Li-Jia Li at Stanford University revealed a new approach to the problem of spotting faces at an angle – including upside down – even when partially occluded.

As the researchers describe in their paper, face detection has been of keen interest over the past few decades.

In 2001, two computer scientists had achieved a breakthrough: Paul Viola and Michael Jones created an algorithm that could pick out faces in an image in real time.

It was fast, and it was simple, working off the realisation that faces have darker and lighter zones: namely, the bridge of the nose typically forms a vertical line that’s brighter than the nearby eye sockets, while the eyes are often in shadow, creating a darker horizontal band, and the cheeks form lighter patches.

The process was called a detector cascade, given that the Viola-Jones algorithm first looks for vertical bright bands in an image that might represent noses, then searches for horizontal dark bands that could be eyes, then searches for other patterns associated with faces, detecting such features in a cascading manner.

As MIT Review reports, the algorithm was so fast and simple that it was soon built into standard point-and-shoot cameras.

But while it worked well when subjects were viewed from the front, it couldn’t handle tilted faces.

The technology has developed over the last 14 years, and the recent breakthrough coming out of the Yahoo/Stanford team is based on a new approach, springing from advances made recently in a type of machine learning known as a deep convolutional neural network.

To train their neural net, Farfade and the other researchers created a database of 200,000 images, including faces at various angles and orientations, plus another 20 million images without faces.

They then fed their neural net batches of 128 images at a time, over 50,000 iterations.

The result is what the team calls the Deep Dense Face Detector: an algorithm that can spot faces set at a wide range of angles, even when partially occluded by other objects, such as the hands and head that are blocking Jolie’s face in the image above.

The algorithm’s results are trumping other algorithms, they said, and do a great job at spotting many faces in one image with high accuracy:

We evaluated the proposed method with other deep learning based methods and showed that our method results in faster and more accurate results.

Of course, while this is good news for all of the cloud providers and social networks that trade in images and in making money from figuring out what those images represent, the privacy implications of fast, automated facial recognition, even in sub-optimal conditions, are staggering.

As it is, Carnegie Mellon has already created a toy drone outfitted with facial recognition software that can identify a face from a far distance, creating a face print as unique as a fingerprint that can be used for any manner of things, whether it’s surveillance by law enforcement or advertisers that use digital billboards for shopping malls that surreptitiously scan shoppers’ faces to determine gender and age.

So much for being just another face in the crowd.

0 Comments

Let’s face it: With all the developments in technology and great interest in collecting and analyzing data, it becomes impossible to protect our privacy in a practical way.

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And as drones proliferate, commercial, surveilance or otherwise ( who will be able to tell the difference from the ground) anticipate the backlash as they start to be used as targets to shoot down in the interest of guarding that privacy, especially by use of catapult ( silent and probably seen as good sport). Ah, Pandora’s Box, how much we have to thank thee for…..

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Sporting would be air soft 20 g. rounds 500 fps or above. You have to mind bystanders especially in urban settings! Besides watching the drone freak out and try to flee as you and a friend or two try to knock it out of the sky using many rounds would be the highlight any hunting season!

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Drones as clay pigeon stand-ins: fun to think about.

Not, however, the best solution. For one thing, drones can clobber people, like the one that fell out of the sky and whacked an Australian triathlete: https://nakedsecurity.sophos.com/2014/04/08/triathlon-camera-drone-falls-out-of-the-sky-owner-claims-it-was-hacked/

Plus, it’s illegal to destroy aircraft: http://www.law.cornell.edu/uscode/text/18/32

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If you wish you could hack together a functional model today using Google image search and a good HD camera. Think of this behind a tellers desk at a bank to validate the identity of a client. or at a restaurant reception desk to be able to identify your VIPs as they enter.

This is not the future, this is the present.

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