Facial recognition systems work by capturing an image, detecting faces, and comparing them to stored templates. Detection involves identifying regions in an image with facial features using algorithms like Haar cascades or deep learning models like SSD.
Once a face is detected, the system extracts unique features, such as distances between facial landmarks. These features are encoded into a numerical representation, often called a face embedding.
The system matches the embedding against a database using techniques like cosine similarity, enabling applications like identity verification or access control.