Scientists in Sweden have developed a liveness-detection system that they say should help reduce the chances of face-biometrics systems being fooled by photographs.
“Liveness is going to be a major issue for biometrics,” says Josef Bigun, a professor of signal analysis who led the research at Halmstad University, in Sweden. This is particularly the case with face recognition. “[Today’s systems] cannot tell the difference between a picture and a face,” he says.
While some systems have rudimentary defences designed to spot photographs, a crook can easily foil them just by bending the picture, says Bigun. Detection systems need to be “a little bit more sophisticated,” he says.
Most face-recognition systems assume that the users will always be accompanied by an official to monitor the process.
But as face biometrics becomes more ubiquitous, this will not always be an option. Some companies, such as the Japanese firm Fujitsu, are already using unattended hand geometry readers to enable people to withdraw cash at ATMs. Face biometrics is likely to follow a similar path, says Bigun.
Michael Jones, a face-recognition researcher at the Mitsubishi Electric Research Laboratories, in Cambridge, MA, believes that face recognition will be more prone to fraud: “It’s so easy to get a photo of a face. You can’t get someone’s irises or fingerprints off the Internet.”
Bigun is trying to combat the problem by using an algorithm that measures the optical flow–a measurement of the 3-D movement of two-dimensional information–to detect how parts of a real face should move in 3-D relative to each other.
Face biometrics currently use two much simpler processes to try to detect liveness. One is to measure how similar the face being presented is to the stored face template of a particular person. Since no two presentations of the same face will look exactly the same, biometrics systems are, somewhat ironically, designed to reject faces that too closely match the original template. So in theory, it may detect a picture if it looks too similar to the original template. But there’s an easy way to get around this, says Bigun: “You simply add statistical noise to an image.” This could be done using a digital copy of the image and basic photo-manipulation software: a user could randomly add dots to the image to introduce small errors.
The second approach uses optical flow to measure the movement of key parts of the face–such as the nose, eyes, and ears–relative to each other. The aim here is to detect slight movements of a photo as the fraudster holds it in front of the camera. If all regions of the image move in a perfectly linear fashion–that is, the nose, eyes, and ears all move in precisely the same way–then the system recognizes that a photo is likely being used.
However, this approach runs the small risk of rejecting a legitimate person if he or she happens to be holding his or her facial expression very still. Also, as mentioned, simply bending a photo can fool these algorithms because it will cause different points of the photo to move at slightly different trajectories from the point of view of the camera, since they are not on the same two-dimensional plane.