Millions of people have tuned in to "24" and watched Jack Bauer take a picture of a suspect's face, send it to his central office and within seconds get an ID of someone on a watch list. Today, this fiction is reality, thanks to the increasing usefulness and effectiveness of facial recognition technology.
But, as is the case with most new-tech advances, there have been some bumps in the road. During early tests in cities like Tampa, Florida, the results were less than stellar. However, companies have made great strides in improving this technology.
In biometrics, the face is more complex than a fingertip, and there are many facial variations that make developing effective technology challenging. And a single face changes over time, adding variables such as hair, scars or wrinkles to the mix.
Today, facial recognition technology can assist officers with identifying suspects as well as finding missing persons. It can be especially helpful for AmberView, which adds functionality to Amber Alerts. Images of missing children can be delivered to a database, accessed by facial recognition programs and sent to officers in the field.Imaging improvement
At its heart, facial recognition technology is about the image. There are a few conditions surrounding images in which the technology must operate. The first and simplest is finding a face in a controlled static background. In this situation, a program uses images with a plain, mono-color background or static background to create facial boundaries. This would be a best-case scenario, such as an image of someone standing still in an elevator. The second scenario involves moving images captured with or without color. In either case, there must be an algorithm that can calculate the moving area and identify facial boundaries and features. A third condition combines both static and movement in a larger area. The third scenario is the most complex and the one that often happens during surveillance situations.
Dr. Jonathan Phillips leads the team that tests facial recognition technology for the National Institute of Standards and Technology (NIST). NIST's latest testing results report a positive change within the technology. "Performance has improved greatly over the last four years," he says, "and there is performance-excellence with higher-resolution images under perfect control."
Phillips predicts the technology will continue to improve, if developers can create systems that reliably and consistently produce higher-resolution images. The software's algorithms can only make the most out of the information presented, and lower resolutions mean less information. "So far 6 megapixels [of resolution] have given us the best results," Phillips points out. "However, it is important to remember it is not purely the number of pixels but other conditions, such as lighting, that influence outcomes."2D versus 3D base images
The NIST report yielded several interesting results. One of the biggest debates surrounding facial recognition is the quality of 3D images vs. 2D ones. Some people believe a 3D image gives a program a more complete picture, while others think high-resolution 2D images offer the most quality.
Phillips addressed this issue in the tests. "We took our results and compared the performance of high-resolution 2D and 3D sensors," he says. "We found the results to be comparable."
Testing revealed that while software programs have advanced greatly, it is the acquisition devices that must now change. "There will be a significant change as there is a greater availability of higher-resolution capture devices at affordable prices," Phillips says. Testing also showed algorithms were equal to and sometimes better than humans at recognizing an unknown face from an image such as a picture.