Word has reached the press that the FBI is launching a $1 billion biometric Next Generation Identification (NGI) system that will comprise of a nationwide database of mugshots, iris scans, DNA records, voice samples, and other biometric data that can help the bureau to identify and catch criminals. Tests revealed that facial recognition systems have reached the point where they can match a single face from a pool of 1.6 million mugshots/passport photos with 92 percent accuracy, in under 1.2 seconds!
The new identification system will enable the FBI to identify persons of interest more easily, as well as to track suspects by picking out their face in a crowd.
The pilot program of NGI only includes mugshots of known criminals, but it's unclear whether that will remain the case once the entire system is rolled out nationwide in 2014.
Ideally, such technological advancements will allow law enforcement to identify criminals more accurately and lead to quicker arrests. But privacy advocates are worried by the broad scope of the FBI's plans. They are concerned that people with no criminal record who are caught on camera alongside a person of interest could end up in a federal database, or be subject to unwarranted surveillance.
Tests in 2010 showed that the best algorithms can pick someone out in a pool of 1.6 million mugshots 92 per cent of the time. It's possible to match a mugshot to a photo of a person who isn't looking at the camera too. Algorithms such as one developed by Marios Savvides's lab at Carnegie Mellon can analyse features of a front and side view set of mugshots, create a 3D model of the face, rotate it as much as 70 degrees to match the angle of the face in the photo, and then match the new 2D image with a fairly high degree of accuracy. The most difficult faces to match are those in low light. Merging photos from visible and infrared spectra can sharpen these images, but infrared cameras are still very expensive.