Dynamic Gesture Recognition—A Machine Vision Based Approach

  • N. S. Sreekanth
  • N. K. Narayanan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)


Computationally simple method for dynamic hand gesture recognition is presented in this paper. The segmentation of hand which take parts in gesture production is being addressed tow different ways using color band based segmentation algorithms. The first one uses the special color stickers as part of finger and the second one does segmentation based on normal skin color. The movement of hand is tracked and Freeman’s eight directional code is generated corresponds to each gestures. A dynamic time wrapping based Levenshtein minimum edit distance algorithm is used for classification. The results of dynamic hand gestures with special color approach and without special color are discussed separately. The accuracy of the system is found to be more for special colour based segmentation than skin colour based segmentation techniques.


Gestures recognition Dynamic gestures 


  1. 1.
    Harshith.C, Karthik.R.Shastry, Manoj Ravindran, M.V.V.N.S Srikanth, Navee Lakshmikhanth “Survey on various Gesture Recognition Techniques for Interfacing Machines based on Ambient Intelligence” (IJCSES) Vol.1, No.2, November 2010.Google Scholar
  2. 2.
    PragatiGarg, Naveen Aggarwal and SanjeevSofat, 2009. Vision Based Hand Gesture Recognition, World Academy of Science, Engineering and Technology 49, pp. 972–977.Google Scholar
  3. 3.
    Laura Dipietro, Angelo M. Sabatini, and Paolo Dario, 2008. Survey of Glove-Based Systems and their applications, IEEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, vol. 38(4), pp. 461–482, doi: 10.1109/TSMCC.2008.923862.
  4. 4.
    Jayshree R. Pansare, Kirti S. Rampurkar, Pritam L. Mahamane, Reshma J. Baravkar, Sneha V. Lanjewar” Real-Time Static Devnagri Sign Language Translation using Histogram “ International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 4, April 2013, pp. 1455–1459.Google Scholar
  5. 5.
    Francis Quek, David McNeilly, Robert Bryll, Susan Duncan, Xin-Feng Ma, Cemil Kirbas, Karl E. McCulloughy, and Rashid Ansari: Gesture and Speech multimodal conversational interaction. ACM Transactions on Computer-Human Interaction, Vol. 9, No. 3, September 2002, pp. 171–193.Google Scholar
  6. 6.
    Hairong Jiang Duerstock, B.S.; Wachs, J.P., “A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments”, Systems, Man, and Cybernetics: Systems, IEEE Transactions on (Volume:44, Issue: 5) May 2014, pp. 630–641.Google Scholar
  7. 7.
    N.S Sreekanth, Gopinath, Supriya Pal, N.K Narayanan. “GESTURE BASED DESKTOP INTERACTION.” International Journal of Machine Intelligence 3.4 (2011): pp. 268–271.
  8. 8.
    Fabio Dominio, Mauro Donadeo, Pietro Zanuttigh. ―Combining multiple depth-based descriptors for hand gesture recognition‖ .Elsevier Publications, Pattern recognitions 2013.Google Scholar
  9. 9.
    Lei Yang, Hui Li, Xiaoyu Wu, Dewei Zhao, Jun Zhai. ― An algorithm of skin detection based on texture‖. IEEE Image and Signal Processing(CSIP), 2011.Google Scholar
  10. 10.
    Ohknishi, A. Nishikawa, Curvature-based segmentation and recognition of hand gestures, Proceedings Annual Conference On Robotics Society of Japan, 1997, pp. 401–407.Google Scholar
  11. 11.
    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 21 (2003)-Elsewhere pp. 745–758.Google Scholar
  12. 12.
    B.J Manikandan, Gowri Shankar, V Anoop, A Datta, V S Chakravarthy: LEKHAK: A System for Online Recognition of Handwritten Tamil Characters. Proceeding of (ICON-2002) Vikas Publishing House Pvt.Ltd. pp. 285–291.Google Scholar
  13. 13.
    Mingyu Chen, Ghassan AlRegib, Biing-Hwang Juang, “Feature Processing and Modeling for 6D Motion Gesture Recognition” IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 3, APRIL 2013, pp. 561–571.Google Scholar
  14. 14.
    Kaustubh S. Patwardhan Sumantra Dutta Roy, “Dynamic Hand Gesture Recognition using Predictive EigenTracker”, Proceedings of. Indian Conference on Computer Vision, Graphics and Image Processing, 2004.
  15. 15.
    Wöllert, Thomas (Dipl.-Inf. (FH)): About Portable Keyboards with Design and Implementation of a Prototype Using Computer Vision, Semester Thesis, Master of Science - Computer Graphics and Image Processing, Munich University of Applied Sciences, Germany. June-2006.Google Scholar
  16. 16.
    Rafael. C Gonzalez, Richard. E Woods, Steven .L Eddin: Digital Image Processing 2/e Pearson Education –Third Indian Reprint -2005, pp. 251.Google Scholar
  17. 17.
    PEER, P., KOVAC, J., AND SOLINA, F. 2003. Human skin colour cluster-ing for face detection. Proceeding of EUROCON 2003.Google Scholar
  18. 18.
    B.J Manikandan, Gowri Shankar, V Anoop, A Datta, V S Chakravarthy: LEKHAK: A System for Online Recognition of Handwritten Tamil Characters. Proceeding of the International Conference on Natural Language Processing (ICON-2002) Vikas Publishing House Pvt. Ltd. pp. 285–291.Google Scholar
  19. 19.
    Algorithm Implementation/String/Levenshtein distance wikibooks

Copyright information

© Springer India 2017

Authors and Affiliations

  1. 1.C-DAC BangaloreBangaloreIndia
  2. 2.Department of Information TechnologyKannur UniversityKannurIndia

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