A Novel Approach for Gait Recognition Based on CC-LSTM-CNN Method

Gait recognition is a new biometric technology to identify different users through gait data. In this paper, a novel CC-LSTM-CNN gait recognition method is proposed based on the cross-correlation (CC) algorithm and the Long Short-Term Memory convolutional neural network (LSTM-CNN) hybrid classificat...

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Bibliographic Details
Published in2021 13th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) pp. 25 - 28
Main Authors Li, Runchao, Song, Chen, Wang, Dongwei, Meng, Fanji, Wang, Yuchen, Tang, Qing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2021
Subjects
Accelerometer
Convolutional Neural Network
Cross-Correlation
Data models
Feature extraction
Gait recognition
LSTM
Solid modeling
Stability analysis
Support vector machines
Three-dimensional displays
Training
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Summary:Gait recognition is a new biometric technology to identify different users through gait data. In this paper, a novel CC-LSTM-CNN gait recognition method is proposed based on the cross-correlation (CC) algorithm and the Long Short-Term Memory convolutional neural network (LSTM-CNN) hybrid classification model. The 3D cross-correlation features of X axis, Y axis and Z axis acceleration signals are calculated by cross-correlation algorithm, and the main features are extracted by Principal Component Analysis. Then LSTM-CNN hybrid model is designed to train the feature data series and get the CC-LSTM-CNN model. The results show that the accuracy for identifying different user reaches 99.3% thru gait data in 5 seconds. The result is significantly improved compared with the traditional machine learning classification algorithm.
DOI:10.1109/IHMSC52134.2021.00014