Bringing Facial Recognition Systems To Light

An enrollment databaseconsisting of faces and names is also created. The faces can also be stored in the form of templates.


The first step in using any facial recognition system is when a probe image
, derived from either a photo or a video, is submitted to the system. The system then detects the face in the image and creates a template.

There are two paths that can be taken

The template derived from the probe image can be compared to a single template in the enrollment database. This “1:1” process is called facial verification.

Alternatively, the template derived from the probe image can be compared to all templates in the enrollment database. This “1:MANY” process is called facial identification.

The system compares two templates – one from the probe image and one from the enrollment database – in order to find a potential match.

A match score is generated for each pair of templates, indicating how similar the two images are.  A match threshold is set by the system’s developer or operator. Any two templates whose match score is above that threshold are considered similar enough to be a potential match.

The match threshold setting is critical because it determines whether a face is included or left out of the results presented to the user.

A lower match threshold will return more matches, with a greater chance of misidentification, known as a false positive. A higher match threshold will return fewer matches, with the possibility that a potential “match” is missed, known as a false negative.

Click and drag the slider to see the importance of match thresholds

Beyond facial recognition

Sometimes facial recognition systems are described as including facial characterization (also called facial analysis) systems, which detect facial attributes in an image, and then sort the faces by categories such as gender, race, or age. These systems are not part of facial recognition systems because they are not used to verify or predict an identity.

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