What is the principle of face image feature extraction?

Feature extraction of face image: The features that can be used in face recognition system are usually divided into visual features, pixel statistical features, face image transformation coefficient features, face image algebraic features and so on. Face feature extraction is aimed at some features of face. Face feature extraction, also known as face representation, is a process of feature modeling of face. The methods of facial feature extraction can be summarized into two categories: one is knowledge-based representation; The other is based on algebraic features or statistical learning. Knowledge-based representation method is mainly based on the shape description of facial organs and the distance characteristics between them, and obtains the feature data that is helpful to face classification. Its feature scores usually include Euclidean distance, curvature and angle between feature points. The face is made up of eyes, nose, mouth, chin and so on. The geometric descriptions of these parts and their structural relationships can be used as important features for face recognition. These features are called geometric features. Knowledge-based face representation mainly includes geometric feature-based method and template matching method. When it comes to face recognition, most people's first reaction is to "brush their faces". Let's first look at the definition of face recognition: face recognition is a biometric technology based on human facial feature information. The video camera or video stream containing faces is used to automatically detect and track faces in the images, and then a series of face-related technologies are carried out on the detected faces, which are usually also called face recognition and face recognition. By enhancing the shadow of the image or reducing the gray value range of the bright area, the overall brightness of the face image is transformed into a predefined standard face image.