Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features
DOI:
https://doi.org/10.14419/ijet.v7i4.11.20806Keywords:
Convolution Neural Network, biometrics, human gait recognition, Kinect, skeletal joints.Abstract
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.
Â
Â
References
[1] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
[2] Arun, P. S. Normal gait cycle. http://boneandspine.com/normal-gait-cycle/.
[3] Stephanie, C. The Kinect sensor. https://electronics.howstuffworks.com/microsoft-kinect2.htm.
[4] Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98-113.
[5] Wang, Y., See, J., Phan, R. C. W., & Oh, Y. H. (2014). Lbp with six intersection points: Reducing redundant information in lbp-top for micro-expression recognition. Proceedings of the Asian Conference on Computer Vision, pp. 525-537.
[6] Sinha, A., Chakravarty, K., & Bhowmick, B. (2013). Person identification using skeleton information from kinect. Proceedings of the International Conference on Advances in Computer-Human Interactions, pp. 101-108.
[7] Sahak, R., Yassin, I., Tahir, N. M., Zaman, F. H. K., & Zabidi, A. (2017). Recognition of human gait in oblique and frontal views using Kinect. Journal of Fundamental and Applied Sciences, 9(4S), 1-18.
[8] MathWorks Inc. 2-D convolutional layer - MATLAB. https://www.mathworks.com/help/nnet/ref/nnet.cnn.layer.convolution2dlayer.html.
[9] Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. https://arxiv.org/pdf/1502.03167.pdf%E7%9A%84paper%E9%80%82%E5%90%88%E6%83%B3%E6%B7%B1%E5%85%A5%E4%BA%86%E8%A7%A3%E5%8E%9F%E7%90%86%EF%BC%8C%E8%BF%99%E4%B8%AA%E8%A7%86%E9%A2%91%E5%BE%88%E6%B8%85%E6%A5%9A%E7%9A%84%E8%AE%B2%E4%BA%86bn%E8%B5%B7%E5%88%B0%E7%9A%84%E4%BD%9C%E7%94%A8%E3%80%82.
[10] MathWorks Inc. Rectified Linear Unit (ReLU) layer - MATLAB. https://www.mathworks.com/help/nnet/ref/nnet.cnn.layer.relulayer.html.
[11] MathWorks Inc. Max pooling layer - MATLAB. https://www.mathworks.com/help/nnet/ref/nnet.cnn.layer.maxpooling2dlayer.html?searchHighlight=max pooling&s_tid=doc_srchtitle.
[12] MathWorks Inc. Fully connected layer - MATLAB. https://www.mathworks.com/help/nnet/ref/nnet.cnn.layer.fullyconnectedlayer.html.
Downloads
How to Cite
Received date: October 3, 2018
Accepted date: October 3, 2018
Published date: October 2, 2018