Human body identity identification based on gait trajectory curve characteristics

Abstract

The invention discloses a human body identity identification method based on gait trajectory curve characteristics. The method comprises the steps that according to curve charts of gait acceleration data in a time domain, a gait identity identification problem is converted into a trajectory curve shape matching problem; from the aspect of iconography, in a paper, a Fourier descriptor is used for describing overall trajectory curve characteristics of rough outlines of gait curves, a concept of a direction angle descriptor is provided to further describe fine local trajectory curve characteristics of the gait curves, and finally the overall trajectory curve characteristics and the fine local trajectory curve characteristics are combined to complete matching of the gait trajectory curves. Itis indicated through results that the gait trajectory curve characteristics provided in the paper can be well applied to identity identification.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a human body identification method based on gait trajectory curve characteristics. Background technique [0002] According to Taiwan's "Electronic Times", statistics from the Ministry of Industry and Information Technology show that by the end of May 2014, the number of mobile phone users in China had reached 1.256 billion, an increase of 0.36% compared to April and an increase of 7.82% over the same period last year, equivalent to 90.8% of people in China are using mobile phones. On May 17, 2016, the Internet Society of China and the National Internet Emergency Response Center (CNCERT) jointly released the "China Mobile Internet Development Status and Security Report (2016)" in Beijing for the first time. According to the report, the number of active mobile Internet users in China reached 780 million in 2015, accounting for 56.9% of the country's population. Mob...

AI Technical Summary

Problems solved by technology

Simple features are extracted in the time domain stage. Although the amount of calculation is small, the recognition rate obtained is not very high; in view of the above problems, the features of the frequency domain are added on this basis, because the frequency domain discards the signal features of the time domain, so generally These two types of features need to be fused, and the recognition rate has been greatly improved after fusion. However, due to the high dimensionality of the features extracted in the frequency domain stage, it is necessary to reduce the dimensionality or optimize the features. The quality of these processes will directly affect the final result

The time-frequency method can better observe information characteristics in time and frequency space, but the redundancy is large

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