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Neural Computing and Applications

, Volume 31, Supplement 1, pp 309–323 | Cite as

Jointly network: a network based on CNN and RBM for gesture recognition

  • Wentao Cheng
  • Ying Sun
  • Gongfa LiEmail author
  • Guozhang Jiang
  • Honghai Liu
Machine Learning Applications for Self-Organized Wireless Networks

Abstract

Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples and the recognition capability of gesture samples in complex background needs to be improved.

Keywords

Kinect sensor Jointly network Static gesture recognition Simulation experiment 

Notes

Acknowledgements

This work was supported by Grants of the National Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 51575412, 61733011, 51505349) and Grants of the National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705). This paper is funded by Wuhan University of Science and Technology graduate students’ short-term study abroad special funds.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Wentao Cheng
    • 1
  • Ying Sun
    • 1
    • 2
  • Gongfa Li
    • 1
    • 3
    Email author
  • Guozhang Jiang
    • 2
    • 4
  • Honghai Liu
    • 5
  1. 1.Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of EducationWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Mechanical Transmission and Manufacturing EngineeringWuhan University of Science and TechnologyWuhanChina
  3. 3.Research Center of Biologic Manipulator and Intelligent Measurement and ControlWuhan University of Science and TechnologyWuhanChina
  4. 4.3D Printing and Intelligent Manufacturing Engineering InstituteWuhan University of Science and TechnologyWuhanChina
  5. 5.School of ComputingUniversity of PortsmouthPortsmouthUK

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