Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features
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2018-10-02 
https://doi.org/10.14419/ijet.v7i4.11.20806
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Convolution Neural Network, biometrics, human gait recognition, Kinect, skeletal joints. -
Biometric identification systems have recently made exponential advancements in term of complexity and accuracy in recognition for security purposes and a variety of other application. In this paper, a Convolutional Neural Network (CNN) based gait recognition system using Microsoft Kinect skeletal joint data points is proposed for human identification. A total of 23 subjects were used for the experiments. The subjects were positioned 45 degrees (oblique view) from Kinect. A CNN based on the modified AlexNet structure was used to fit the different input data size. The results indicate that the training and testing accuracies were 100% and 69.6% respectively.
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How to Cite
Shahrum Md Guntor, M., Sahak, R., Zabidi, A., Md Tahir, N., Mohd Yassin, I., Ismael Rizman, Z., Baharom, R., & Abdul Wahab, N. (2018). Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features. International Journal of Engineering & Technology, 7(4.11), 202-205. https://doi.org/10.14419/ijet.v7i4.11.20806
