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2018 Shenzhen wide dynamic face recognition system SDK technical scheme-Preferred Halo International

by:PTKSAI     2020-02-15
Face and other biological features of the human body (Fingerprint, iris, etc) As innate, its * sex and good characteristics that are not easy to copy provide the necessary premise for identity authentication. Compared with other types of biometric recognition, face recognition has the following characteristics: non-mandatory: users do not need to cooperate with face acquisition equipment, and can acquire face images almost unconsciously. This sampling method is not mandatory; Non-Contact: users can obtain face images without direct contact with the device; Concurrency: in practical application scenarios, multiple faces can be sorted, judged and identified; In addition, it also conforms to the visual characteristics: the characteristics of recognizing people by appearance, as well as the characteristics of simple operation, intuitive results and good concealment. Face recognition is a biological recognition technology based on human facial feature information. Using a camera or camera to collect images or video streams containing human faces, and automatically detect and track human faces in the images, thus carrying out a series of related technologies on the detected human faces, it is also commonly called Portrait Recognition and facial recognition. Technical process face recognition system mainly includes four components, namely: face image acquisition and detection, face image preprocessing, face image feature extraction and matching and recognition. FACE image acquisition and detection face image acquisition: different face images can be collected through the camera lens, for example, static images, dynamic images, different positions, different expressions and other aspects can be well collected. When the user is within the shooting range of the acquisition device, the acquisition device will automatically search for and capture the user's face image. Face detection: in practice, face detection is mainly used for preprocessing of face recognition, I . e. accurately calibrating the position and size of the face in the image. Face images contain rich pattern features, 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. For some features of the face. Face feature extraction, also known as face representation, is the process of feature modeling of faces. The methods of facial feature extraction can be divided into two categories: one is knowledge-based representation method; The other is a representation method based on algebraic features or statistical learning. The knowledge-based representation method mainly obtains feature data that is helpful for face classification according to the shape description of face organs and the distance characteristics between them, its feature components usually include Euclidean distance, curvature and angle between feature points. Human face is composed of eyes, nose, mouth, chin and other parts. Geometric description 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 methods and template matching methods. FACE image matching and recognition face image matching and recognition: the extracted feature data of the face image is searched and matched with the feature template stored in the database. By setting a threshold, when the similarity exceeds this threshold, the matching result is output. Face recognition is to compare the face features to be recognized with the obtained face feature templates, and judge the identity information of the face according to the similarity. This process is divided into two categories: one is confirmation, which is a process of one-to-one image comparison, and the other is recognition, which is a process of one-to-many image matching and comparison.
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