JAMES A. BAKER, R. ALTA CHARO, et al.

Facial recognition technology (FRT) is an increasingly prevalent tool for automated identification and identity verification of individuals. Its speed and accuracy have improved dramatically in the past decade. Its use speeds up identification tasks that would otherwise need to be performed manually in a slower or more labor-intensive way and, in many use cases, makes identification tasks practical that would be entirely infeasible without the use of these tools. FRT measures the pairwise similarity of digital images of human faces to estab- lish or verify identity. It uses machine learning models to extract facial features from an­ ­ image, creating what is known as a template. It then compares these templates to compute a similarity score. In one-to-one comparison, the claimed identity of a single individual is verified by comparing the template of a captured probe image with an exist- ing reference image (is this person who they say they are?). In one-to-many comparison, an individual is identified by comparing the template of a captured face image to the templates for many individuals contained in a database of reference images known as a gallery (what is the identity of the unknown person shown in this image?). FRT accuracy is affected by image quality. Good quality is associated with coopera- tive capture in which the subject is voluntarily facing a good camera at close range with good lighting. Good lighting is especially important to give correct contrast in subjects with darker skin tones. Non-cooperative capture, in which subjects may not even realize that their image is being captured, such as images taken from security cameras, gener- ally results in lower-quality images.

National Academies Sciences Engineering Medicine, (2024), 160 pages