Facial recognition system

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A facial recognition system is a computer-driven application for automatically identifying a person from a digital image. It does that by comparing selected facial features in the live image and a facial database.

It is typically used for security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.

Popular recognition algorithms include eigenface, fisherface, the Hidden Markov model, and the neuronal motivated Dynamic Link Matching. A newly emerging trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition. Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. Tests on the FERET database, the widely used industry benchmark, showed that this approach is substantially more reliable than previous algorithms.

1 Notable users and deployments
- 1.1 Additional uses
2 Criticisms
- 2.1 Efficacy
- 2.2 Privacy concerns
3 Early development
4 Comparative study
5 References
6 See also
7 External links
- 7.1 Commercial vendors
- 7.2 Applications

[edit] Notable users and deployments

The London Borough of Newham, in the UK, has a facial recognition system built into their borough-wide CCTV system.

The German Federal Police use a facial recognition system to allow voluntary subscribers to pass fully automated border controls at Frankfurt Rhein-Main international airport. Subscribers need to be European Union or Swiss citizens.[1]

Griffin Investigations is famous for its recognition system used by casinos to catch card counters and other blacklisted individuals.

[edit] Additional uses

In addition to being used for security systems, authorities have found a number of other applications for facial recognition systems.

At Super Bowl XXXV in January 2000, police in Tampa Bay, Florida, used FaceIt to search for potential criminals and terrorists in attendance at the event.<ref name="Bonsor">Bonsor, K.. How Facial Recognition Systems Work. Retrieved on 2006-06-18.</ref>

In the 2000 presidential election, the Mexican government employed facial recognition software to prevent voter fraud. Some individuals had been registering to vote under several different names, in an attempt to place multiple votes. By comparing new facial images to those already in the voter database, authorities were able to reduce duplicate registrations.<ref name="Bonsor"/> Similar technologies are being used in the United States to prevent people from obtaining fake identification cards and driver’s licenses.<ref name="ap2006">"State Agency Uses Facial Recognition Software to Fight Fake ID’s", Associated Press via The Janesville Gazette, 2006-05-08. Retrieved on 2006-06-25.</ref>

There are also a number of potential uses for facial recognition that are currently being developed. For example, the technology could be used as a security measure at ATM’s; instead of using a bank card or personal identification number, the ATM would capture an image of your face, and compare it to your photo in the bank database to confirm your identity. This same concept could also be applied to computers; by using a webcam to capture a digital image of yourself, your face could replace your password as a means to log-in.<ref name="Bonsor"/>

[edit] Criticisms

[edit] Efficacy

Critics of the technology complain that the London Borough of Newham scheme has, as of 2004, never recognised a single criminal, despite several criminals in the system's database living in the Borough and the system having been running for several years. "Not once, as far as the police know, has Newham's automatic facial recognition system spotted a live target."<ref>Meek, James. "Robo cop", UK Guardian newspaper, 2002-06-13.</ref><ref>Krause, Mike. "Is face recognition just high-tech snake oil?", Enter Stage Right, 2002-01-14.</ref> This information seems to conflict with that given by Identix's press release of April 2001, where they claim the system was credited with a 34% reduction in crime - which better explains why the system was then rolled out to Birmingham also.

An experiment by the local police department in Tampa, Florida, had similarly disappointing results.^{[citation needed]}

"Camera technology designed to spot potential terrorists by their facial characteristics at airports failed its first major test at Boston's Logan Airport"<ref>Willing, Richard. "Airport anti-terror systems flub tests Face-recognition technology fails to flag 'suspects'", USA Today, 2003-09-02.</ref>

[edit] Privacy concerns

Despite the potential benefits of this technology, many citizens are concerned that their privacy will be invaded. Some fear that it could lead to a “total surveillance society,” with the government and other authorities having the ability to know where you are, and what you are doing, at all times.<ref>Civil Liberties & Facial Recognition Software. Retrieved on 2006-06-18.</ref>

[edit] Early development

Pioneers of Automated Facial Recognition include: Woody Bledsoe, Helen Chan Wolf, and Charles Bisson.

During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson, worked on using the computer to recognize human faces (Bledsoe 1966a, 1966b; Bledsoe and Chan 1965). He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published. Given a large database of images (in effect, a book of mug shots) and a photograph, the problem was to select from the database a small set of records such that one of the image records matched the photograph. The success of the method could be measured in terms of the ratio of the answer list to the number of records in the database. Bledsoe (1966a) described the following difficulties:

“	This recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, etc. Some other attempts at facial recognition by machine have allowed for little or no variability in these quantities. Yet the method of correlation (or pattern matching) of unprocessed optical data, which is often used by some researchers, is certain to fail in cases where the variability is great. In particular, the correlation is very low between two pictures of the same person with two different head rotations.	„
—Woody Bledsoe, 1966

This project was labeled man-machine because the human extracted the coordinates of a set of features from the photographs, which were then used by the computer for recognition. Using a graphics tablet (GRAFACON or RAND TABLET), the operator would extract the coordinates of features such as the center of pupils, the inside corner of eyes, the outside corner of eyes, point of widows peak, and so on. From these coordinates, a list of 20 distances, such as width of mouth and width of eyes, pupil to pupil, were computed. These operators could process about 40 pictures an hour. When building the database, the name of the person in the photograph was associated with the list of computed distances and stored in the computer. In the recognition phase, the set of distances was compared with the corresponding distance for each photograph, yielding a distance between the photograph and the database record. The closest records are returned.

This brief description is an oversimplification that fails in general because it is unlikely that any two pictures would match in head rotation, lean, tilt, and scale (distance from the camera). Thus, each set of distances is normalized to represent the face in a frontal orientation. To accomplish this normalization, the program first tries to determine the tilt, the lean, and the rotation. Then, using these angles, the computer undoes the effect of these transformations on the computed distances. To compute these angles, the computer must know the three-dimensional geometry of the head. Because the actual heads were unavailable, Bledsoe (1964) used a standard head derived from measurements on seven heads.

After Bledsoe left PRI in 1966, this work was continued at the Stanford Research Institute, primarily by Peter Hart. In experiments performed on a database of over 2000 photographs, the computer consistently outperformed humans when presented with the same recognition tasks (Bledsoe 1968). Peter Hart (1996) enthusiastically recalled the project with the exclamation, "It really worked!"

[edit] Comparative study

Among the different biometric techniques facial recognition may not be the most reliable and efficient but its great advantage is that it does not require aid from the test subject. Properly designed systems installed in airports, multiplexes, and other public places can detect presence of criminals among the crowd. Other biometrics like fingerprints, iris, and speech recognition cannot perform this kind of mass scanning. However, questions have been raised on the effectiveness of facial recognition software in cases of railway and airport security.