Apparently people have sought to defeat facial recognition systems by substituting a photograph, then inviting the system to read it like a real individual. When the system seemed to call for something more, like movement, they just bent the photo a little bit, to which the system responded with something like, “ok, you must be real.” Here's the thing about photographs: they look a lot like the individual they represent. So what's a face recognition system to do?
Technology Review
reports: “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. [...] 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. [...]
Bigun's approach takes the optical-flow concept a step further. “We looked at how a 3-D face moves,” he says. By comparing how bent photos of faces and real faces move, the researchers were able to identify differences in the trajectories of key facial points. For example, the movement of an ear and nose as a head turns slightly will be different from those appearing on a bent photo. This is because the parts of the face in the photo are still on a single plane, even if the photo is bent; conversely, the trajectories of 3-D facial features are more complex and follow a particular pattern relative to each other. Using this information, the researchers created a system to detect such discrepancies. [...]
According to Bigun, the only way of beating the system he helped develop would be to make an accurate 3-D mask of someone's face. While it's feasible that someone with connections to Hollywood makeup artists could do this, it's pretty unlikely, says Mitsubishi's Jones. “It's just not practical for the random criminal.”“
Their software then enables a security guard simply to point at an object like a briefcase and have the system sort through and assemble all examples of pictures with the same object to find the ones with the best view. This solves the essential problem with surveillance: it typically produces too much visual information to process in real time.
lectronic license plate scanners do “in 30 seconds what usually takes an officer an entire day,” or at least that's the claim. That's estimated to be about 1,500 license checks compared to a max of about 40 in a day in the past.
