A pedestrian identification method based on mobile phone inertial sensor

Abstract

The invention provides a pedestrian identification method based on a mobile phone inertial sensor. The core of the present invention is a deep neural network model, which consists of three convolutional layers, two LSTM units, an attention mechanism module and a fully connected layer. After learning and training, the model can be used in smart phones. In the inertial sensor data, the hidden biometric information is mined to realize the identity verification of the mobile phone carrier. The method of the invention has the advantages of small amount of data and fast calculation speed, and with the rapid development of electronic components such as sensors in recent years, the required cost is also relatively low.

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 biometric identification. Background technique [0002] Biometric identification technology refers to the use of the inherent physiological characteristics and behavioral characteristics of the human body to verify personal identity. At present, most biometric identification technologies are to identify the iris, fingerprint or face of a person. 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 recognition technologies based on inertial sensors use traditional machine learning methods. Due to the limitations of the size and power consumption of wea...

AI Technical Summary

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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