Facial Recognition (FR)

Face recognition algorithms, however, have no built-in notion of a particular person. They are not built to identify particular people; instead they include a face detector followed by a feature extraction algorithm that converts one or more images of a person into a vector of values that relate to the identity of the person. The extractor typically consists of a neural network that has been trained on ID-labeled images available to the developer. In operations, they act as generic extractors of identity-related information from photos of persons they have usually never seen before. Recognition proceeds as a differential operator: Algorithms compare two feature vectors and emit a similarity score. This is a vendor-defined numeric value expressing how similar the parent faces are. It is compared to a threshold value to decide whether two samples are from, or represent, the same person or not. Thus, recognition is mediated by persistent identity information stored in a feature vector (or “template”).