Face recognition system usually includes several processes: face image acquisition and detection, key point extraction, face regularization (image processing), face feature extraction and face recognition comparison.
Face image acquisition. Different face images can be captured by the camera lens, such as static images, dynamic images, different positions, different expressions and other aspects can be well captured. When the user is within the capture range of the capture device, the capture device will automatically search and capture the user's face image.
Face Detection. Face detection is mainly used in practice for the preprocessing of face recognition, i.e., to accurately calibrate the position and size of the face in the image.
Key point extraction (feature extraction). The features that can be used in a face recognition system are usually categorized into visual features, pixel statistical features, face image transform coefficient features, face image algebraic features and so on. Face feature extraction is done for certain features of the face. Face feature extraction, also known as face characterization, is the process of feature modeling of a face. The methods of face feature extraction are summarized into two main categories: one is the knowledge-based characterization method; the other is the characterization method based on algebraic features or statistical learning.
Face regularization (preprocessing). Image preprocessing for faces is the process of processing images and ultimately serving feature extraction based on face detection results. The original image acquired by the system is often not directly usable due to the limitations of various conditions and random interference, and it must be subjected to image preprocessing such as grayscale correction and noise filtering in the early stages of image processing. For face images, the preprocessing process mainly includes light compensation, gray scale transformation, histogram equalization, normalization, geometric correction, filtering and sharpening of face images.
Face recognition comparison (matching and recognition). The feature data of the extracted face image is searched and matched with the feature templates stored in the database, and the results obtained from the matching are output by setting a threshold, when the similarity exceeds this threshold. Face recognition is to compare the face features to be recognized with the obtained face feature template, and judge the identity information of the face according to the degree of similarity. It can be divided into 1:1, 1:N, and attribute recognition. Among them, 1:1 is to compare the eigenvalue vectors corresponding to 2 faces, and 1:N is to compare the eigenvalue vectors of 1 face photo with the eigenvalue vectors corresponding to another N faces, and output the face with the highest degree of similarity or the top X of similarity ranking.