Speed gate wing gate channel gate is one of the channel management equipment

   The speed gate wing gate channel gate is one of the channel management equipment, which is mainly used for pedestrian channel management; it has the characteristics of fast opening, safety and convenience, and is an ideal management and grooming equipment for pedestrian high frequency access channels. The speed-recognition wing gate channel gate adopts face recognition speed-pass wing gate technology is a technological innovation. For users, it not only simplifies management, but also improves safety. Let's take a look at what are the characteristics of dynamic face recognition.

According to the facial feature inspection skills: after selecting colors, generalization, texture, structure or histogram features, etc., face inspection.
Face matching skills based on template matching: Get face templates from the database, and then adopt a certain template matching strategy to match the captured face pictures with the template library, and determine the person by the level of correlation and the size of the template. Face size and orientation information.
Face-checking skills based on calculation: After collecting a lot of pictures about "faces" and "non-faces", a positive and negative sample library of faces is selected, and the calculation method is used to strengthen the training system, and then complete the face and non-person Check and classify the face patterns. Domestic type This famous face recognition passenger flow calculation has Yaliang stores.
Face recognition features of wing gate passage gate of fast pass gate
Geometrical features: The features are based on the interval and ratio between face points. The recognition speed is fast, the memory request is relatively small, and the sensitivity to light decreases.
According to the model features: according to the different probabilities of the different feature conditions, the features of the face drawing are obtained.
Based on calculation features: Treat face pictures as random vectors, and use calculation methods to identify different face feature patterns. Typical features include characteristic faces, independent component analysis, and singular value decomposition.
According to the characteristics of the neural network: many neural units are used to associatively store and recall the features of the face picture, and the accurate recognition of the face picture is completed according to the probability of different neural unit conditions.