Manufacturers and advocates of face recognition products have grown accustomed to fielding questions—and sometimes fending off attacks—about the technology. Isn’t it a violation of personal privacy? Hasn’t it been shown unreliable? Is it really necessary for security? These cultural and technological concerns have kept face recognition from realizing its full potential, but they may not hold it back much longer.
The American Civil Liberties Union has demonstrated the most serious opposition to face recognition technology within the United States. Their main argument has been that widespread surveillance is likely to become increasingly invasive and abusive over time. They have frequently referred to some highly publicized airport installation fiascos from several years back, as well as the infamous 2001 Super Bowl incident in Tampa, FL, buttressing their criticism by noting that since face recognition “does not even work,” it doesn’t really increase safety and security.
This question of accuracy has been the biggest sticking point. Face recognition has worked fairly well in controlled environments, but has had serious problems in more realistic, uncontrolled settings. Orientation and lighting variations, and to some degree changes in expression, can produce markedly different facial images for the same person. Changes in hairstyle, facial hair and body weight, the effects of aging, and deliberate disguise can also hamper performance.
Given these problems, why choose face recognition? The truth is, face recognition is still the best passive and non-intrusive biometric available. It’s also easy to use: An operator needs neither special hardware nor expert skills to interpret the results.
In recent years, attacks on face recognition seem to be dampening. In part, this is due to the public’s growing understanding and acceptance of video surveillance generally and familiarity with extensive surveillance efforts outside the U.S., particularly in venues like London.
Additionally, after well over a decade of gradual improvements, the technology has begun to show signs of maturation as it quietly gains ground by becoming more reliable.
New, Improved, Accurate
Face recognition advocates have actively pursued major developments that are now leading to increased accuracy.
3-D Representation. Faces are part of a 3-D world, so representing them with 3-D data makes intuitive sense. There are two main ways to do that. In one alternative, structured near-visible infrared light is projected on a face. A 3-D representation is constructed based on the distortions of the pattern of light on the face. This technique is used by A4Vision.
An alternative method employs two or more cameras and builds a 3-D shape via correspondence analysis and triangulation methods. This is Geometrix’s approach.
Both of these systems claim sub-millimeter accuracy. Because they need high resolution to achieve good results, they work best when the person is a couple of feet away from the capture device(s). The most attractive part of 3-D technologies is that they virtually eliminate the effects of orientation and illumination changes, the two major difficulties associated with traditional 2-D recognition techniques. On the other hand, 3-D systems are typically more expensive than their 2-D counterparts, and their computational load is quite a bit higher as well.
High-Resolution 2-D. Due to advances in sensor technology, today’s cameras are able to acquire increasingly higher-resolution face images. You can expect better recognition results from a system that captures more details on a face. Although skin recognition technology is supposed to work with mid-range cameras, higher resolution helps the analysis of skin patterns on the face.
The addition of skin pattern data can improve face recognition performance. That promise was most likely behind Identix’s acquisition of DeLean Vision in 2004. Indeed, Identix claims that face recognition performance improves by 20 to 25% when skin pattern data is included.