jm + face-recognition   3

Who’s using your face? The ugly truth about facial recognition
In order to feed this hungry system, a plethora of face repositories — such as IJB-C — have sprung up, containing images manually culled and bound together from sources as varied as university campuses, town squares, markets, cafés, mugshots and social-media sites such as Flickr, Instagram and YouTube.

To understand what these faces have been helping to build, the FT worked with Adam Harvey, the researcher who first spotted Jillian York’s face in IJB-C. An American based in Berlin, he has spent years amassing more than 300 face datasets and has identified some 5,000 academic papers that cite them.

The images, we found, are used to train and benchmark algorithms that serve a variety of biometric-related purposes — recognising faces at passport control, crowd surveillance, automated driving, robotics, even emotion analysis for advertising. They have been cited in papers by commercial companies including Facebook, Microsoft, Baidu, SenseTime and IBM, as well as by academics around the world, from Japan to the United Arab Emirates and Israel.

“We’ve seen facial recognition shifting in purpose,” says Dave Maass, a senior investigative researcher at the EFF, who was shocked to discover that his own colleagues’ faces were in the Iarpa database. “It was originally being used for identification purposes . . . Now somebody’s face is used as a tracking number to watch them as they move across locations on video, which is a huge shift. [Researchers] don’t have to pay people for consent, they don’t have to find models, no firm has to pay to collect it, everyone gets it for free.”
data  privacy  face-recognition  cameras  creative-commons  licensing  flickr  open-data  google  facebook  surveillance  instagram  ijb-c  research  iarpa 
4 weeks ago by jm
London police’s use of AFR facial recognition falls flat on its face
A “top-of-the-line” automated facial recognition (AFR) system trialled for the second year in a row at London’s Notting Hill Carnival couldn’t even tell the difference between a young woman and a balding man, according to a rights group worker invited to view it in action. Because yes, of course they did it again: London’s Met police used controversial, inaccurate, largely unregulated automated facial recognition (AFR) technology to spot troublemakers. And once again, it did more harm than good.

Last year, it proved useless. This year, it proved worse than useless: it blew up in their faces, with 35 false matches and one wrongful arrest of somebody erroneously tagged as being wanted on a warrant for a rioting offense.

[...] During a recent, scathing US House oversight committee hearing on the FBI’s use of the technology, it emerged that 80% of the people in the FBI database don’t have any sort of arrest record. Yet the system’s recognition algorithm inaccurately identifies them during criminal searches 15% of the time, with black women most often being misidentified.
face-recognition  afr  london  notting-hill-carnival  police  liberty  met-police  privacy  data-privacy  algorithms 
september 2017 by jm
Schneier on Automatic Face Recognition and Surveillance
When we talk about surveillance, we tend to concentrate on the problems of data collection: CCTV cameras, tagged photos, purchasing habits, our writings on sites like Facebook and Twitter. We think much less about data analysis. But effective and pervasive surveillance is just as much about analysis. It's sustained by a combination of cheap and ubiquitous cameras, tagged photo databases, commercial databases of our actions that reveal our habits and personalities, and ­-- most of all ­-- fast and accurate face recognition software.

Don't expect to have access to this technology for yourself anytime soon. This is not facial recognition for all. It's just for those who can either demand or pay for access to the required technologies ­-- most importantly, the tagged photo databases. And while we can easily imagine how this might be misused in a totalitarian country, there are dangers in free societies as well. Without meaningful regulation, we're moving into a world where governments and corporations will be able to identify people both in real time and backwards in time, remotely and in secret, without consent or recourse.

Despite protests from industry, we need to regulate this budding industry. We need limitations on how our images can be collected without our knowledge or consent, and on how they can be used. The technologies aren't going away, and we can't uninvent these capabilities. But we can ensure that they're used ethically and responsibly, and not just as a mechanism to increase police and corporate power over us.
privacy  regulation  surveillance  bruce-schneier  faces  face-recognition  machine-learning  ai  cctv  photos 
october 2015 by jm

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