Facial recognition system

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

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.

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

Critics of the technology complain that the LB 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. An experiment by the local police department in Tampa, Florida, had similarly disappointing results.

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.

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 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!"

Facial Recognition System Process
In order to verify the identity of an individual, a facial recognition system goes through various steps. The first step involves capturing an image of the individual’s face with the use of a video camera. An algorithm called local feature analysis (LFA) is then used to match distinctive features of the individual’s face to images contained in the system (Dunn, R., 2003).

These distinctive features are called nodal points. There are approximately 80 nodal points on the human face, such as the distance between the eyes, the shape of jaw line, and the shape of the chin (Dunn, R., 2003). Generally, facial recognition systems account for slight variations in these nodal points that may occur due to changes in facial expressions (Webb, W., 2001).

After scanning a database of images and finding a match, the facial recognition system rates the possibility of the correct match, relying on the information it has processed (Dunn, R., 2003). Since many systems have the ability to recognize an individual’s face within seconds, a correct match may be determined while the individual is still within a specific area (Webb, W., 2001).

FaceIt Facial Recognition System
In 2003, Identix Inc. was named as one of the most reliable facial recognition software companies. Since then, the company has further developed its product, FaceIt®ARGUS (Dizard III, 2003). FaceIt® can provide face finding, face recognition, liveness (verification of an actual human being), and image quality services (Identix Inc., 2005).

As mentioned above, many facial recognition systems take advantage of Local Feature Analysis (LFA); however, FaceIt is the only technology to utilize both LFA and skin biometrics, uniqueness of skin texture (Identix Inc., 2005).

FaceIt software is generally not affected by facial expressions, gender, accessories, or hair. However, if any of these factors cover a large portion of an individual’s face, the software performance may be affected (Identix Inc., 2005).

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 (Bonsor, n.d.).

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 (Bonsor, n.d.). Similiar technologies are being used in the United States to prevent people from obtaining fake identification cards and driver’s licenses (Associated Press, 2006).

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 (Bonsor, n.d.).

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 (Civil Liberties, n.d.).

References:
Associated Press (2006). State Agency Uses Facial Recognition Software to Fight Fake ID’s. The Janesville Gazette, May 8, 2006. Retrieved June 25, 2006 from http://www.gazetteextra.com/dmv_facialrecog050806.asp

Bonsor, K. (n.d.). How Facial Recognition Systems Work. Retrieved June 18, 2006, from http://computer.howstuffworks.com/facial-recognition.htm

Civil Liberties & Facial Recognition Software (n.d.). Retrieved June 18, 2006 from http://terrorism.about.com/od/civillibertiesissues/i/facialrecsoft_2.htm

Dizard III, W. P. (March 2003). NIST rates facial recognition systems. Retrieved June	25, 2006 from http://www.gcn.com/online/vol1_no1/21408-1.html

Dunn, R. (September 2003). Intelligent Video. Security. Troy:.Vol.40, Iss. 8; pg. 52. Retrieved June 25, 2006 from ProQuest Database http://proquest.umi.com.ezproxy.library.dal.ca/pqdweb?did=406985271&sid=1&Fmt=4&clientId=15814&RQT=309&VName=PQD

Identix Inc. (2005). FaceIt Argus. Retrieved June 24, 2006 from http://www.identix.com/products/pro_security_bnp_argus.html

Webb, W. (Dec 20, 2001). Friend or foe?. Vol.46, Iss. 28; pg. 32, 4 pgs. EDN. Retrieved June 20, 2006 from ProQuest Database http://proquest.umi.com/pqdweb?index=2&did=96791054&SrchMode=1&sid=2&Fmt=4&VInst=P