Facial recognition systems are commonly used for security purposes but are increasingly being used in a variety of other applications. The Kinect motion gaming system, for example, uses facial recognition to differentiate among players. Some mobile payment systems use facial recognition to securely authenticate users, and facial recognition systems are currently being studied or deployed for airport security.
Most current facial recognition systems work with numeric codes called faceprints. Such systems identify 80 nodal points on a human face. In this context, nodal points are end points used to measure variables of a person’s face, such as the length or width of the nose, the depth of the eye sockets and the shape of the cheekbones. These systems work by capturing data for nodal points on a digital image of an individual’s face and storing the resulting data as a faceprint. The faceprint can then be used as a basis for comparison with data captured from faces in an image or video.
Facial recognition systems based on faceprints can quickly and accurately identify target individuals when the conditions are favorable. However, if the subject’s face is partially obscured or in profile rather than facing forward, or if the light is insufficient, the software is less reliable. Nevertheless, the technology is evolving quickly and there are several emerging approaches, such as 3D modeling, that may overcome current problems with the systems. According to the National Institute of Standards and Technology (NIST), the incidence of false positives in facial recognition systems has been halved every two years since 1993 and, as of the end of 2011, was just .003%
Currently, a lot of facial recognition development is focused on smartphone applications. Smartphone facial recognition capacities include image tagging and other social networking integration purposes as well as personalized marketing. A research team at Carnegie Mellon has developed a proof-of-concept iPhone app that can take a picture of an individual and -- within seconds -- return the individual's name, date of birth and social security number.
Facebook uses facial recognition software to help automate user tagging in photographs. Here’s how facial recognition works in Facebook: Each time an individual is tagged in a photograph, the software application stores information about that person’s facial characteristics. When enough data has been collected about a person to identify them, the system uses that information to identify the same face in different photographs, and will subsequently suggest tagging those pictures with that person’s name.
Facial recognition software also enhances marketing personalization. For example, billboards have been developed with integrated software that identifies the gender, ethnicity and approximate age of passersby to deliver targeted advertising.
According to a report from CBS News, almost half of United States citizens are represented in a facial recognition database.
Kim Komando explains facial recognition technology: