Facial biometrics recognition is a technique for determining or validating an individual’s identification based on their face. Face recognition systems can identify individuals in photographs, videos, and real-time. During police stops, law enforcement may use mobile devices to identify individuals. However, facial recognition data might be prone to inaccuracy, potentially implicating innocent individuals in crimes they did not commit. Particularly poor at detecting African Americans and other ethnic minorities, women, and young people, facial recognition software often misidentifies or fails to recognize individuals, negatively affecting specific groups disproportionately.
Face recognition has also been used to target individuals participating in protected speech. Face recognition technology will certainly become more widespread in the near future. It may be used to follow the travels of humans around the globe, similar to how automatic license plate scanners track automobiles based on plate numbers. Real-time mobile facial biometric verification is already used in other nations.
Face recognition systems rely on computer algorithms to identify the unique characteristics of a person’s face. These characteristics, like the distance between the eyes and the curvature of the chin, are then transformed into a mathematical representation and compared to data on other faces stored in a face recognition database. The data on a specific face is frequently referred to as a face template and is unique from an image since it is meant to incorporate just the distinguishing characteristics of a face.
Instead of positively recognizing an unknown individual, many face recognition systems are intended to produce a probability match score between the unknown person and specified face templates kept in a database. Instead of delivering a single result, these systems will provide a list of probable matches that are rated according to the probability that they are accurate identification.
Face recognition as an identity verification system differs in its ability to identify individuals in difficult situations, such as bad lighting, low picture resolution, and a suboptimal viewing angle. A “false negative” occurs when a face recognition system fails to match an individual’s face to a database-contained picture. In other words, the system returns zero results in error in response to a query. While “false positives” occur when a face recognition system matches a person’s face to a database picture, the match is erroneous.
When investigating a facial recognition system, it is crucial to consider both the “false positive” and “false negative” rates since there is nearly always a trade-off between them. For instance, if you are using facial recognition to unlock your phone, it is preferable if the system fails to identify you a few times than if it incorrectly identifies other individuals as you and allows them to unlock your phone.
To know more about facial biometrics, specifically about protecting America’s infrastructure from cyberattacks, below is an infographic from authID.