AI News, Facial recognition systems trained on millions of photos of people ... artificial intelligence
US intelligence wants to use your face to train AI systems
The US government research unit serving intelligence agencies is looking to expand its ability to monitor thousands of people at once, with a new request for companies, cities, and the academic sector to help compile a massive video dataset.
Artificial-intelligence algorithms like the ones the government wants to train require large amounts of data to be accurate. “Further research in the area of computer vision within multi-camera networks may support post-event crime scene reconstruction, protection of critical infrastructure and transportation facilities, military force protection, and in the operations of National Special Security Events,” the IARPA posting explains.
Additional images are generated by Department of Homeland Security staff posing as civilian travelers, similar to what IARPA is proposing for its street project. IARPA is asking potential partners to address the use of “non-interacted pedestrians,” people on sidewalks who will have no idea they are participating in the project: specifically, how to “plan and prepare for human subject research collection” and its necessary protocols.
“Cities are putting up these camera networks all over the place and they tell the public that it’s being used for one thing—crime control, traffic congestion—but they don’t tell them that the data is going to be used to train surveillance systems,” says Maass, adding that IARPA is “asking the question, ‘Can this be done,’ rather than asking, ‘Should this be done?’” Correction: This story has been updated to correct that NIST does not train algorithms, but helps test them.
Huge leaps in AI have made facial recognition smarter than your brain
Royal Caribbean Cruises has begun using facial recognition systems to speed passengers on their way through security and ID checks.
Propelling the spread of facial recognition systems are huge leaps in artificial intelligence, the technology that seeks to give computers some of the ability, versatility and even creativity of human thinking.
The biggest improvements have come through a specific area of AI called neural networks, inspired by the actual workings of human brain cells.
Hardware and software improvements enabled an approach called deep learning -- multiple layers of digital neurons that provide increasingly refined image analysis.
As we teach computers those skills, our interactions with them become more convenient -- less like submitting database commands and more like dealing with the natural world in which we evolved.
In a training phase, neural networks scrutinize vast numbers of images of faces, learning on their own what's important in the recognition process.
That representation can be compared rapidly with those of other faces, letting a facial recognition system decide if a person entering an office is on an authorized employee list or raise an alert when a potential shoplifter also appears on police arrest records.
To work well, facial recognition systems need images with well-illuminated, clear faces that give a neural network detailed, accurate data.
That's why passport photos require even lighting, plain backgrounds, neutral expressions and subjects facing straight toward the camera.
One way to reduce errors is to tune the system by pushing some of the data apart to make it clearer for the neural net, reducing the likelihood of a false positive, said Marios Savvides, director of the CyLab Biometrics Center at Carnegie Mellon University.
Savvides' team is also blending modern AI with an older approach called correlation filters that allows neural networks to improve facial recognition accuracy when faces are obscured, poorly lit or facing away from the camera.
Another way to improve facial recognition is to pair it with other attributes, such as fingerprints, voice prints and other biometric data, or factors such as passwords.
That might not work well when a system is just scanning people walking into a store, but it's pretty common for controlled situations where people are logging into a network.
Unisys' facial recognition system alone is 99 percent accurate, but with an approach it calls fusion that blends in other biometric factors, the company reaches 99.9 percent or 99.99 percent accuracy.
if you train a system using images of mostly white people, a common practice, the system might have difficulty recognizing people of color.
Northwell Health, which serves 3.5 million patients and is the largest health care provider in New York, is using a facial recognition program to streamline patient visits, reduce clerical errors and ultimately improve health.
In emergencies like car accidents, the system would be able to identify an unconscious patient so that nurses and doctors could find medical histories and family contacts.
- On Friday, July 19, 2019
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