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.

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.