Facial recognition Technology

 

Facial Recognition

Facial recognition technology is based upon the spatial geometry of an individual’s face, measuring the distance between key features such as eyes, nose, chin, mouth, etc., and within this category, there are solutions that have been developed utilizing both 2-dimensional and 3-dimensional views.

Facial recognition technologies leverage existing facial image libraries and infrastructure (including captured digital images and CCTV surveillance solutions), often making them faster to deploy at a lower cost than other biometrics. This type of technology is non-intrusive, and can be used in covert applications. However, facial recognition technology is generally perceived as less accurate (susceptible to spoofing, unable to account for the impact of changes over time, including facial hair and glasses) than other biometric applications, and there are a great number of privacy concerns with covert applications (e.g., watch list surveillance in airports and other public locations).

With widespread use of facial recognition technology for international travel documents, vendors are expected to focus on improving accuracy levels. The largest deployment of facial recognition technology encompasses database sizes approaching 40 million individuals.

Key Applications:

  • Watch list Surveillance
  • Benefits/Asylum Fraud Reduction
  • Identity Verification – International Travel Documents (passports, visas)
  • Multi-modal systems, in combination with fingerprint recognition

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Facial biometric matching is used to verify the identity of individuals attempting access for various border management and access control applications. Facial matching algorithms make use of digital photographs of the face stored in a database or on an ID card. These digital images are captured upon registration into the system, and then compared to a live photo of the individual upon an access attempt in a process called “matching”. The performance of the matching algorithm can be improved—that is, the occurrence of false matches and false accepts can be reduced—if the quality of the facial images can be maximized. For this reason, new standards for biometric facial images specify normative requirements for facial image quality, and also provide best practices for biometric facial image capture.

 :: How is facial recognition technology currently being used?
Unlike other biometric systems, facial recognition can be used for general surveillance, usually in combination with public video cameras. There have been three such uses of face-recognition in the U.S. so far. The first is in airports, where they have been proposed - and in a few cases adopted - in the wake of the terrorist attacks of September 11. Airports that have announced adoption of the technology include Logan Airport in Boston, T.F. Green Airport in Providence, R.I., and San Francisco International Airport and the Fresno Airport in California.

A second use of the technology was at the 2001 Super Bowl in Tampa, where pictures were taken of every attendee as they entered the stadium through the turnstiles and compared against a database of some undisclosed kind. The authorities would not say who was in that database, but the software did flag 19 individuals. The police indicated that some of those were false alarms, and no one flagged by the system was anything more than a petty criminal such as a ticket scalper. Press reports indicate that NewOrleans authorities are considering using it again at the 2002 Super Bowl.

The technology has also been deployed by a part of Tampa, Ybor City, which has trained cameras on busy public sidewalks in the hopes of spotting criminals. As with the Super Bowl, it is unclear what criteria were used for including photos in the database. The operators have not yet caught any criminals. In addition, in England, where public, police-operated video cameras are widespread, the town of Newham has also experimented with the technology.
:: Facial Recognition: How it Works
Facial recognition utilizes distinctive features of the face - including the upper outlines of the eye sockets, the areas surrounding the cheekbones, the sides of the mouth, and the location of the nose and eyes - to perform verification and identification. Most technologies are somewhat resistant to moderate changes in hairstyle, as they do not utilize areas of the face located near the hairline. When used in identification mode, facial recognition technology generally returns candidate lists of close matches as opposed to returning a single definitive match (as do fingerprint and iris-scan technologies). 

Image Quality

The performance of facial recognition technology is very closely tied to the quality of the facial image. Low-quality images are much more likely to result in enrollment and matching errors than high-quality images. For example, many photograph databases associated with drivers' licenses or passports contain photographs of marginal quality, such that importing these files and executing matches may lead to reduced accuracy. Similarly well-known problems exist with surveillance deployments. If facial images for enrollment and matching can be acquired from live subjects with high-quality equipment, system performance increases substantially. For facial recognition at slightly greater-than-normal distances, there is a strong correlation between camera quality and system capabilities.

Facial Scan Process Flow

As with all biometrics, 4 steps - sample capture, feature extraction, template comparison, and matching - define the process flow of facial scan technology. Enrollment generally consists of a 20-30 second enrollment process whereby several pictures are taken of one's face. Ideally, the series of pictures will incorporate slightly different angles and facial expressions, to allow for more accurate matching. After enrollment, distinctive features are extracted (or global reference images are generated), resulting in the creation of a template. The template is much smaller than the image from which it is derived: facial images can require 15-30kb, templates range from 84 bytes to 3000 bytes. The smaller templates are normally used for 1:N matching.

Verification and identification follow the same steps. Assuming your audience is a cooperative audience (as opposed to uncooperative or non-cooperative), the user 'claims' an identity through a login name or a token, stands or sits in front of the camera for a few seconds, and is either matched or not matched. This comparison is based on the similarity of the newly created match template against the reference template or templates on file. The point at which two templates are similar enough to match, known as the threshold, can be adjusted for different personnel, PC's, time of day, and other factors.

Verification vs. Identification

System design for facial scan verification vs. identification differ in a number of ways. The primary difference is that identification does not utilize a claimed identity. Instead of employing a PIN or user name, then delivering confirmation or denial of the claim, identification systems attempt to answer the question "Who am I?" If there are only a handful of enrollees in the database, this requirement is not demanding; as databases grow very large, into the tens and hundreds of thousands, this task becomes much more difficult. The system may only be able to narrow the database to a number of likely candidates. Human intervention may then be required at the final verification stages.

A second variable in identification is the dynamic between the target subjects and capture device. In verification, one assumes a cooperative audience, one comprised of subjects who are motivated to use the system correctly. Facial scan systems, depending on the exact type of implementation, may also have to be optimized for non-cooperative and uncooperative subjects. Non-cooperative subjects are unaware that a biometric system is in place, or do not care, and make no effort to either be recognized or to avoid recognition. Uncooperative subjects actively avoid recognition, and may use disguises or take evasive measures. Facial scan technologies are much more capable of identifying cooperative subjects, and are almost entirely incapable of identifying uncooperative subjects.

Primary Facial Recognition Technologies

The four primary methods employed by facial recognition vendors to identify and verify subjects include eigenfaces, feature analysis, neural network, and automatic face processing. Some types of facial scan technology are more suitable than others for applications such as forensics, network access, and surveillance.

"Eigenface," roughly translated as "one's own face," is a technology patented at MIT which utilizes two dimensional, global grayscale images representing distinctive characteristics of a facial image. Variations of eigenface are frequently used as the basis of other face recognition methods.

 

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