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Accurate the acquisition of face images.
Feature extraction: Extract representative information from the original image to distinguish individuals. Commonly used feature extraction methods include principal component analysis (PCA) and linear discriminant analysis (LDA), etc. These methods can convert face images into low-dimensional feature vectors for subsequent feature matching.
Feature matching: Compare the collected face feature vectors with the stored templates. Templates are face feature information pre-registered in the system. The goal of feature matching is to find the closest template and determine whether there is a match. In order to speed up feature matching, facial recognition machines usually use fast matching algorithms, such as Euclidean distance, cosine similarity, etc.
Authentication: Determine whether to pass the verification according to the matching degree. If the matching degree is higher than the set threshold, the authentication is successful; otherwise, the authentication fails.
Functional Features
Contactless Operation: Users do not need to touch the device, they only need to pass in front of the access control machine to complete the authentication, which improves the convenience and hygiene of use.
High Precision Identification: Advanced facial recognition algorithms are adopted, which can accurately identify face features and ensure the accuracy and security of authentication.
Multiple Verification Methods: In addition to facial recognition, some all-in-one machines also support fingerprint, password, card swiping and other verification methods to meet the needs of different scenarios.
Real-time Monitoring and Alarm: Linkage with the intelligent security system, real-time monitoring of entry and exit personnel, and real-time alarm when encountering abnormal situations.
Convenient management: Support uploading and downloading user information and attendance records through U disk, TCP/IP network, etc., which is convenient for managers to conduct data statistics and analysis.
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