Biological characteristic motion mode intelligent identification method and application thereof

Abstract

The invention provides a biological characteristic motion mode intelligent identification method and application thereof, and the method comprises the following steps: S1, obtaining the body motion parameter data of a user through an intelligent terminal motion parameter sensor, and forming multi-dimensional data; S2, performing data analysis on a user body motion parameter data set and continuously training a machine learning model to form a first database of user body motion parameter feature vectors; S3, obtaining the user body motion image data set through a video image collection mode, and forming a second database of user body motion image feature vectors after the continuous training of the machine learning model; S4, decomposing the first database of the user body motion parameterfeature vectors according to the forward motion and lateral motion parameter feature vectors, and then performing interval mapping training with the second database; and S5, judging a mapping matchingresult, performing mapping verification according to the user body motion parameter feature vectors, and meanwhile, inputting a body motion parameter decomposition feature vector third database.

Description

technical field [0001] The invention relates to the field of computers, in particular to an intelligent recognition method of a biometric motion mode and an application thereof. Background technique [0002] With the rapid development of big data and artificial intelligence technology, recognition technologies based on biometric features such as face, fingerprint, iris, and gait have been widely used. In engineering practice, because the recognition technology of biometric features such as face, fingerprint, iris, and gait is mainly based on 2D space for analysis and recognition, there will be many restrictions when carrying out accurate individual recognition in actual application scenarios: First, limited Changes in conditions such as angle, illuminance, observation distance, and the degree of cooperation of the target object during image data collection seriously affect the recognition accuracy; second, the conscious or subconscious cooperation of the target object is req...

AI Technical Summary

Problems solved by technology

In engineering practice, because the recognition technology of biometric features such as face, fingerprint, iris, and gait is mainly based on 2D space for analysis and recognition, there will be many restrictions when carrying out accurate individual recognition in actual application scenarios: First, limited Changes in conditions such as angle, illuminance, observation distance, and the degree of cooperation of the target object during image data collection seriously affect the recognition accuracy; second, the conscious or subconscious cooperation of the target object is required, and behaviors such as makeup, wearing a mask, and carrying belongings often lead to The efficiency of some individual recognition patterns is extremely reduced; the third is that the information expression based on 2D space is too intuitive, it is difficult to collect and store comparison sample data, and the personal information of citizens is easily stolen, which leads to limited application scenarios

[0003] Up to now, although the neural network machine model based on deep learning has made breakthroughs in face, fingerprint, iris and other biometric recognition, the collection of massive learning samples, comparison sample collection and large-scale model training are still headaches for the majority of engineers. The problem

In addition, the final model training results and data feature value expression are also extremely abstract for engineers, and there is always a lack of an optimal identification paradigm

Especially in gait recognition, due to the lack of effective methods and research approaches for biological motion pattern recognition, in addition to some preliminary application research on gait recognition in China, other biological motion pattern recognition, such as gesture movement or The recognition and application research of other special sports rarely involves

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