Gait recognition method and system in combination with subspace learning and tesnor neural network

Abstract

The invention discloses a gait recognition method and system in combination with subspace learning and tensor neural network and belongs to the field of intelligent recognition. The method includes the following steps: acquiring gait data, obtaining a gait data set, and processing the gait data set to obtain a silhouette set, and further obtaining a gait energy diagram; taking 80% of the silhouettes as a training set and conducting dimensionality reduction on the training set, taking the rest 20% of the silhouettes as testing set data and testing the result of the training, then extracting features from the gait energy diagram and a data tensor neural network module which is subject to dimensionality reduction, then classifying the features through a support vector machine which acts as a classifier, finally comparing the training set and the result of the test set to obtain the result from identifying and verifying the identity of passenger. According to the invention, the method is easy to implement and has low cost. The method can automatically conduct identity authorization and fake identity verification in a certain venue, and effectively increase the effectiveness of identity verification for security and protection and other conditions in a monitored venue.

Description

technical field [0001] The invention relates to the field of intelligent recognition, in particular to a gait recognition method and system combining subspace learning and tensor neural network. Background technique [0002] In order to better protect the property safety and information security of individuals, families and enterprises, many places are equipped with safety protection measures. Traditional security mainly relies on manpower, such as setting up guards for registration, organizing personnel to patrol, etc. Since this method cannot guarantee that there are guards in each area at any time, it is prone to security loopholes. In recent years, with the rapid development of computer, network, image processing and transmission technology, gait recognition technology has shown its value more and more. Through the processing of video materials, gait recognition image data can be obtained, which can avoid fingerprint forgery, mask anti-face recognition, etc. Due to the ...

AI Technical Summary

Problems solved by technology

[0003] However, most of the current personnel identification is essentially a human monitoring method, that is, by videotaping the environment in the monitoring area and relying on the naked eyes of the monitoring personnel to find abnormalities, it is impossible to accurately detect people in certain situations , cannot avoid counterfeiting measures such as fingerprint imitation and wearing a mask,

This artificial monitoring method has high cost, poor flexibility, large limitations, high requirements for human body activities and wear, and cannot meet the real needs of users, that is, some precise identification needs.

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