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A Computer Vision-Based Gesture Recognition Using Hidden Markov Model

  • Keshav SinhaEmail author
  • Rashmi Kumari
  • Annu Priya
  • Partha Paul
Conference paper

Abstract

One of the unique features present in the human being is a gesture. Nowadays, the human gesture is used to interact with the computer which will reduce the workload. The gesture recognition consists of various types of human interfaces such as facial, finger, iris, hand. In this paper, we proposed a hand gesture recognition using hidden Markov model (HMM) and trajectory code word. There are various papers presented on this topic by using different methods, whereas our technique uses the four phases for recognition: (i) tracking of real-time gesture and segmentation, (ii) feature extraction using the Gaussian model, (iii) HMM likelihood for forward algorithm, and (iv) gesture recognition using Viterbi decoding. Because of using the inbuilt function of OpenCV and MATLAB, it would be very easy to develop the system. The experimental results show the accuracy of 99%, in recognition rate, whereas it is quite faster while using for real-time application.

Keywords

HMM Viterbi decoding Hand recognition Gaussian model 

References

  1. 1.
    F.K.H. Quek: “Toward a Vision-Based Hand Gesture Interface,” Virtual Reality Software and Technology Conf., pp. 17–31, Aug. 1994.Google Scholar
  2. 2.
    A. Kendon: “Current Issues in the Study of Gesture,” The Biological Foundations of Gestures: Motor and Semiotic Aspects, J. -L. Nespoulous, P. Peron, and A. R. Lecours, Eds., pp. 23–47. Lawrence Erlbaum Assoc., 1986.Google Scholar
  3. 3.
    D. McNeill and E. Levy: “Conceptual Representations in Language Activity and Gesture,” Speech, Place and Action: Studies in Deixis and Related Topics, J. Jarvella and W. Klein, Eds. Wiley, 1982.Google Scholar
  4. 4.
    F.K.H. Quek: “Eyes in the Interface,” Image and Vision Computing, vol. 13, Aug. 1995.Google Scholar
  5. 5.
    L. Nianjun, C. L. Brian, J. K. Peter, and A. D. Richard: “Model Structure Selection & Training Algorithms for an HMM Gesture Recognition System,” In International IWFHR, pp. 100–106, 2004.Google Scholar
  6. 6.
    S. L. Phung, A. Bouzerdoum, and D. Chai: “A Novel Skin Color Model in YCbCr Color Space and its Application to Human Face Detection,” In IEEE International Conference on Image Processing (ICIP), pp. 289–292, 2002.Google Scholar
  7. 7.
    Matthias Rehm, Nikolaus Bee, Elisabeth André: “Wave Like an Egyptian — Accelerometer Based Gesture Recognition for Culture Specific Interactions,” Published by the British Computer Society, 2007.Google Scholar
  8. 8.
    H. Lee and J. Kim: “An HMM-Based Threshold Model Approach for Gesture Recognition,” IEEE Trans. TPAMI, Vol. 21 (10), pp. 961–973, 1999.Google Scholar
  9. 9.
    D. B. Nguyen, S. Enokida, and E. Toshiaki: “Real-Time Hand Tracking and Gesture Recognition System,” IGVIP05 Conference, CICC, pp. 362–368, 2005.Google Scholar
  10. 10.
    Md Iqbal Quraishi, Krishna Gopal Dhal, J Paul Choudhury, Pulak Ghosh, Pranav Sai, Mallika De, “A Novel Human Hand, Finger Gesture Recognition using Machine Learning,” 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, 2012.Google Scholar
  11. 11.
    Mahmoud Elmezain, Ayoub Al-Hamadi, Jorg Appenrodt, Bernd Michaelis: “A Hidden Markov Model-Based Continuous Gesture Recognition System for Hand Motion Trajectory,” IEEE, 2008.Google Scholar
  12. 12.
    Tianding Chen: “Novel Machine Learning in Hand Gesture Recognition Using Multiple View,” IITA International Conference on Control, Automation and Systems Engineering, 2009.Google Scholar
  13. 13.
    Vladimir I. Pavlovic, Rajeev Sharma, Thomas S. Huang: “Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997.Google Scholar
  14. 14.
    Feng-Sheng Chen, Chih-Ming Fu, Chung-Lin Huang: “Hand gesture recognition using a real-time tracking method and hidden Markov models,” Image and Vision Computing, Vol- 21, pp- 745–758, 2003.CrossRefGoogle Scholar
  15. 15.
    SEO Yul Kim, Hong Gul Han, Jin Woo Kim, Sanghoon Lee: “A Hand Gesture Recognition Sensor Using Reflected Impulses,” IEEE Sensors Journal, 2016.Google Scholar
  16. 16.
    Danling Lu, Yuanlong Yu, and Huaping Liu: “Gesture Recognition Using Data Glove: An Extreme Learning Machine, Method,” Proceedings of the 2016 IEEE, International Conference on Robotics and Biomimetics Qingdao, China, December 3–7, 2016.Google Scholar
  17. 17.
    Gonzalo Pomboza-Junez, Juan Holgado Terriza: “Hand Gesture Recognition based on sEMG signals using Support Vector Machines,” International Conference on Consumer Electronics-Berlin, 2016.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Keshav Sinha
    • 1
    Email author
  • Rashmi Kumari
    • 1
  • Annu Priya
    • 1
  • Partha Paul
    • 1
  1. 1.Department of Computer Science EngineeringBirla Institute of Technology, MesraRanchiIndia

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