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Identity recognition method and system based on photoplethysmography

A technology of photoplethysmography and identity recognition, which is applied in the field of identity recognition, can solve problems such as non-promotable, unable to guarantee feature reliability, troublesome process, etc., to avoid feature extraction operations, simplify feature extraction process, and simplify complexity Effect

Inactive Publication Date: 2019-11-15
广东玖智科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method needs to extract a large number of features, the process is very troublesome, and the reliability of the extracted features cannot be guaranteed, so it is not generalizable

Method used

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  • Identity recognition method and system based on photoplethysmography
  • Identity recognition method and system based on photoplethysmography

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Experimental program
Comparison scheme
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Embodiment 1

[0024] This embodiment realizes the identification method based on photoplethysmography.

[0025] Convolutional Neural Networks (CNN) is a type of feed-forward neural network that includes convolution calculations and has a deep structure. It is one of the representative algorithms for deep learning. The convolutional neural network has the ability to learn representations, and can perform translation-invariant classification of input information according to its hierarchical structure, so it is also called "translation-invariant artificial neural network".

[0026] The SoftMax model is a model used to solve multi-classification problems.

[0027] In a multi-layer neural network, there is a functional relationship between the output of the upper layer node and the input of the lower layer node. This function is called the activation function (also known as the activation function). sigmoid and tanh are "saturated activation functions", while ReLU and its variants are "non-sat...

Embodiment 2

[0043] This embodiment implements an identification system based on photoplethysmography.

[0044] attached figure 2 System block diagram of the identification method based on photoplethysmography. Identity recognition system based on photoplethysmography, said system includes data preprocessing program module, generalized S transform program module, Getframe image processing program module, convolutional neural network, said data preprocessing program module, generalized S transform program module, Getframe The image processing program module and the convolutional neural network are sequentially connected. The above-mentioned convolutional neural network includes a convolutional layer, a pooling layer, an activation function layer, and a fully connected layer. The identification method based on photoplethysmography outputs the identification result.

Embodiment 3

[0046] This embodiment realizes the identification method based on photoplethysmography. This embodiment is a specific application of Embodiment 1 and Embodiment 2.

[0047] Please refer to the attached figure 2 . attached figure 2 It is a system block diagram of the identification method based on photoplethysmography in this embodiment, which includes four parts: data preprocessing, generalized S transform, Getframe technology and convolutional neural network.

[0048] Please refer to the attached figure 1 . figure 2 It is a step diagram of the identification method based on photoplethysmography in this embodiment. The data processing flow is as follows: the first step is to input the PPG signal in the time domain for filtering; the second step is to obtain the PPG spectrum characteristic map through the generalized S transform; the third step is to obtain the PPG spectrum trace characteristic map through Getframe; the fourth step is to obtain the PPG spectrum trace ...

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Abstract

The invention relates to an identity recognition method based on photoplethysmography. The identity recognition method comprises the following steps that S1, a data preprocessing program module preprocesses collected PPG signals; S2, a generalized S transformation program module performs generalized S transformation on the preprocessed PPG signal to obtain a PPG spectrum feature map; S3, a Getframe image processing module calls a Getframe function to snapshot the PPG spectrum feature map at each time point, and a continuous PPG spectrum trajectory feature map is obtained; and S4, the convolutional neural network performs feature extraction and feature classification on the PPG frequency spectrum track feature map to realize identity recognition. The method has the beneficial effects that the PPG signal is converted into the two-dimensional image from the original one-dimensional signal, so that identity recognition is carried out by adopting the convolutional neural network subsequently, the data features are easy to extract, the data feature extraction process is simple, and the reliability is high.

Description

【Technical field】 [0001] The invention relates to the technical field of identification, in particular to an identification method and system based on photoplethysmography. 【Background technique】 [0002] With the rapid development of informatization and the popularization of Internet applications, information security issues have become more and more prominent, and the requirements for personal identification have become more and more intense. Only by accurately identifying personal identities can an automated system effectively protect information security. Traditional identity verification methods have the risk of being lost, stolen, or even forged, and their security is low. As an inherent feature of the human body, biometrics not only overcome the shortcomings of traditional verification methods, but are also easier to identify and verify automatically. Biometrics commonly used to verify identity include fingerprints, face, voice, skin, gait, iris, veins, hand shape, h...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06F21/32
CPCG06F21/32G06N3/045G06F18/21G06F18/24G06F18/214
Inventor 王国兴王敏林炳辉
Owner 广东玖智科技有限公司
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