How Face Recognition AI Works: How detectors perceive the photos of people

Face recognition AI analyses and compares patterns in a person's facial features, such as the distance between the eyes, nose, and mouth, the shape of the jawline, and the depth of the eye sockets. These patterns are then compared to a database of known faces to find a match. The technology uses a combination of algorithms, including deep learning and neural networks, to analyze and compare facial features. The process typically involves capturing an image or video of a person's Face, extracting facial features, and then comparing them to a database of known faces to find a match.

Facial recognition system uses detectors to analyze images and extract facial features. These detectors are typically based on deep learning algorithms, such as convolutional neural networks (CNNs), which are trained on large datasets of facial images.

During the detection process, the image is first passed through a series of convolutional and pooling layers, which reduce the image's resolution and extract important features. Next, the image is passed through a series of fully connected layers, which analyze the extracted features and identify specific facial characteristics, such as the distance between the eyes, the jawline's shape, and the eye sockets' depth.

Once the facial features have been extracted, they are compared to a database of known faces to find a match. The process is typically repeated multiple times with different algorithms and parameters to improve accuracy and reduce the chances of false positives.

In summary, Face recognition AI uses deep learning algorithms to detect and analyze facial features in an image, then compares these features to a database of known faces to find a match.