Comparative Analysis of 1-D HMM and 2-D HMM for Hand Motion Recognition Applications

  • K. Martin Sagayam
  • D. Jude Hemanth
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)


Hand motion recognition is an interesting field in the development of virtual reality applications through the human–computer interface. The stochastic mathematical model hidden Markov model (HMM) is used in this work. There are numerous parametric efforts in HMM for temporal pattern recognition. To overcome the recursiveness in the forward and backward procedures, dimensionality and storage problem in Markov model, 2-D HMM has been used. The experimental results show the comparison of 2-D HMM with 1-D HMM in terms of performance measures.


Hand motion recognition HCI HMM 1-D HMM 2-D HMM 


  1. 1.
    Mokhtar M. Hasan and Pramoud K. Misra, Brightness factor matching for Gesture Recognition system using Scaled Normalization, International Journal of Computer Science & Information Technology (IJCSIT), vol. 3, no. 2, April 2011.Google Scholar
  2. 2.
    Sushmita Mitra and Tinku Acharya, Gesture Recognition: A Survey, IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Review, vol. 37, no. 3, pp. 311–324, 2007.Google Scholar
  3. 3.
    Mokhtar M. Hasan and Pramoud K. Misra, Robust Gesture Recognition using Euclidian Distance, IEEE International Conference on Computer and Computational Intelligence, China, vol. 3, pp. 38–46, 2010.Google Scholar
  4. 4.
    Mokhtar M. Hasan and Pramoud K. Misra, HSV Brightness factor matching for Gesture Recognition system, International Journal of Image Processing, Malaysia, vol. 4, no. 5, pp. 456–467, 2010.Google Scholar
  5. 5.
    P. A. Devijver, Modeling of digital images using hidden Markov mesh random fields, Signal Processing IV: Theories and Applications, pp. 23–28, 1998.Google Scholar
  6. 6.
    J. Li, A. Najimi and R. M. Gray, Image classification by a two-dimensional hidden Markov model, IEEE Trans. on Signal Processing 48, pp. 517–533, 2000.Google Scholar
  7. 7.
    D. Schonfeld and N. Bouaynaya, A new method for multidimensional optimization and its application in image and video processing, IEEE Signal Processing Letters 13, pp. 485–488, 2006.Google Scholar
  8. 8.
    Yuri Grinberg and Theodore J. Perkins, State Sequence Analysis in Hidden Markov Model, grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).Google Scholar
  9. 9.
    Raman, Himanshu, Study and Comparison of Various Image Edge Detection Techniques, International Journal of Image Processing (IJIP), vol. 3, Issue 1, 2009.Google Scholar
  10. 10.
    Arindam Misra, Abe Takashi, Takayuki Okatani, Koichiro Deguchi, Hand Gesture Recognition using Histogram of Oriented Gradients and Partial Least Squares Regression, IAPR Conference on Machine Vision Applications, Japan, pp. 479–482, 2011.Google Scholar
  11. 11.
    Neha V. Tavari, A. V. Deorankar, Indian Sign Language Recognition based on Histograms of Oriented Gradient, International Journal of Computer Science and Information Technologies, vol. 5(3), ISSN: 0975-9646, 2014.Google Scholar
  12. 12.
    Bobulski. J, Kubanek. M, Person identification system using sketch of the suspect, Optica Applicata, 4(42), pp. 865–873, 2012.Google Scholar
  13. 13.
    Janusz Bobulski, Comparison of the effectiveness of 1D and 2D HMM in the pattern recognition, Image Processing & Communication, vol. 19, no. 1, pp. 5–12, 2015.Google Scholar
  14. 14.
    Jai Li, Gray, Robert M., Image Segmentation and Compression using Hidden Markov Model, Springer International Series in Engineering and Computer Science, 2000.Google Scholar
  15. 15.
    Rabiner. L.R., A tutorial on hidden Markov models and selected application in speech recognition, Proceedings of the IEEE, 77(2), pp. 257–285, 1989.Google Scholar
  16. 16.
    Bobulski. J, Adrjanowicz. L, Part I. In Artificial Intelligence and Soft Computing, Springer publication, pp. 515–523, 2013.Google Scholar
  17. 17.
    E. de Souza e Silva, R. M. M. Leao and Richard R. Muntz, Performance Evaluation with Hidden Markov Models, International Federation for Information Processing (IFIP), LNCS 6821, pp. 112–128, 2011.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of ECEKarunya UniversityCoimbatoreIndia

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