Pedestrian identity recognition method based on mobile phone inertial sensor

An inertial sensor and identification technology, applied in the field of deep neural network, can solve the problems of ineffective data mining, big data noise, low recognition effect, etc., to achieve the effect of fast calculation speed, small amount of data, and not easy to camouflage

Active Publication Date: 2020-03-27
WUHAN UNIV
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Problems solved by technology

However, most of the existing identification technologies based on inertial sensors use traditional machine learning methods. Due to the limitations of the size and power consumption of wearable sensors, the collected data will have large data noise, which makes traditional methods unable to To effectively carry out data mining, it is necessary to manually extract the features in the sensor data sequence, and the information that these features can express is limited, and the final recognition effect is very low, and prior experience is often added to assist judgment

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  • Pedestrian identity recognition method based on mobile phone inertial sensor
  • Pedestrian identity recognition method based on mobile phone inertial sensor
  • Pedestrian identity recognition method based on mobile phone inertial sensor

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[0026] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] The technical core of the present invention is a deep neural network model, such as figure 1 As shown, the model consists of three convolutional layers, two LSTM units, an attention mechanism module and a fully connected layer. The first convolution layer contains 64 one-dimensional convolution kernels with a length of 25, and the second and third convolution layers each contain 64 one-dimensional convolution kernels with a length of 21. The hidden values ​​in the two LSTM units The number of neurons in each layer is 128, and the number of neurons in the output layer of the fully connected layer is equal to the number of pedestrian identities to be identified. After a sample of size (128,6) is input to the first convolutional layer, a feature map FM of size (104,6,64) is obtained 1 , FM 1 Input to the second convolut...

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Abstract

The invention provides a pedestrian identity recognition method based on a mobile phone inertial sensor. The core of the method is a deep neural network model. The model is composed of three convolution layers, two LSTM units, an attention mechanism module and a full connection layer, and after learning and training, the model can mine hidden biological characteristic information in inertial sensor data contained in the smart phone to realize identity verification of a mobile phone carrier. The method disclosed by the invention has the advantages of small data volume and high calculation speed, and the required cost is relatively low due to rapid development of electronic components such as sensors and the like in recent years.

Description

technical field [0001] The invention describes a deep neural network method for pedestrian identity verification based on mobile phone inertial sensors, and belongs to the technical field of biological feature recognition. Background technique [0002] Biometric identification technology refers to the use of the inherent physiological and behavioral characteristics of the human body to verify personal identity. This technology is not easy to forge or be stolen, and is safe and convenient. At present, most biometric identification technologies are to identify people's irises, fingerprints or faces. Most of these existing technologies are based on images, and the calculation speed is slow. Due to the low cost of sensors, small amount of data and easy calculation, biometric identification methods based on inertial sensors have more potential for development. However, most of the existing identification technologies based on inertial sensors use traditional machine learning met...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/32G06K9/00G06K9/62G06N3/04
CPCG06F21/32G06N3/045G06F2218/12G06F18/2193G06F18/214
Inventor 苏今腾郭迟罗亚荣余佩林张沪寅顾宇
Owner WUHAN UNIV
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