Face Recognition

Face Recognition is a biometric technology for identification based on human facial feature information. Face detection uses a camera or camera to collect images or video streams containing faces, and automatically detects and tracks faces in the images, and then performs face recognition on detected faces. A series of related technologies, usually also called face identifier, face reader,face scanner.

The Facial Recognition system mainly includes four components: face image acquisition and detection, face image preprocessing, face image feature extraction, and matching and recognition.

Facial scanner(face identifier): different facial images can be collected through the camera lens, such as static images, dynamic images, different positions, different expressions, etc. can be well collected. When the user is within the shooting range of the collection device, the collection device will automatically search for and capture the user’s face image.

Face detection (facial reader): Face detection is mainly used for preprocessing of face recognition in practice, that is, to accurately mark the position and size of the face in the image. The pattern features contained in the face image are very rich, such as histogram features, color features, template features, structural features, and Haar features. Face detection is to pick out the useful information and use these features to realize face detection.

The mainstream face detection method uses the Adaboost learning algorithm based on the above characteristics. The Adaboost algorithm is a method for classification. It combines some weaker classification methods to form a new strong classification method. .

In the process of face recognition (face recognition), the Adaboost algorithm is used to select some rectangular features (weak classifiers) that best represent the face, and the weak classifier is constructed into a strong classifier according to the weighted voting method, and then the training obtained Several strong classifiers are connected in series to form a cascaded classifier, which effectively improves the detection speed of the classifier.

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