Microsoft claims its facial recognition technology just got a little less awful. Earlier this year, a study by MIT researchers found that tools from IBM, Microsoft, and Chinese company Megvii could correctly identify light-skinned men with 99-percent accuracy. But it incorrectly identified darker-skinned women as often as one-third of the time. Now imagine a computer incorrectly flagging an image at an airport or in a police database, and you can see how dangerous those errors could be. Microsoft's software performed poorly in the study.
If you're reading this in the United States, there's a 50 percent chance that a photo of your face is in at least one database used in police facial-recognition systems. Police departments in nearly half of U.S. states can use facial-recognition software to compare surveillance images with databases of ID photos or mugshots. Some departments only use facial-recognition to confirm the identity of a suspect who's been detained; others continuously analyze footage from surveillance cameras to determine exactly who is walking by at any particular moment. Altogether, more than 117 million American adults are subject to face-scanning systems. These findings were published Tuesday in a report from Georgetown Law's Center for Privacy and Technology.
A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.
OSAKA – Despite advances in facial recognition technology, the police in Osaka still rely on pure skill to find fugitives, with investigators using only their memory to arrest dozens of wanted criminals every year. While other police forces in the world have "super recognizer" units that hunt down fugitives, the so-called miatari (look and hit) technique used in Osaka has contributed to the arrests of over 4,000 criminals in Japan since the Osaka Prefectural Police introduced it as a formal investigative method in November 1978. There has not been a single wrongful arrest. "The best part of this method is being able to detect fugitives who are hard to find in normal investigations," said a senior investigator in Osaka. He says a forensic analysis is an imperative part of criminal investigations, but "we want to pass on the tradition because our job is to make sure no one gets away with a crime."