Skip to main content
SpringerLink
Log in

A Fuzzy Framework for Real-Time Gesture Spotting and Recognition

  • Published:
Journal of Russian Laser Research Aims and scope

Abstract

A vital requirement of any recognition system claiming to be real time is the capability to perform feature extraction in real time. In this paper, we propose an innovative fuzzy approach for real-time dynamic gesture recognition and spotting, where a compact local descriptor is designed to model moving gesture skeletons as a time series of fuzzy statistical features. Then, a set of one-vs-rest SVMs is trained on these features for gesture recognition and spotting. In this approach, the meaningful hand movements are successfully spotted while concurrently removing unintentional hand movements from an input video sequence. When evaluated on a gesture data set incorporating a relatively large and diverse collection of video data, the method proposed yields promising results that compare very favorably with those reported in the literature, while retaining real-time performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Y. Tu, C. Kao, and H. Lin, “Human computer interaction using face and gesture recognition,” in: IEEE Conference on Signal and Information Processing Association Annual Summit (APSIPA-2013), p. 1.

  2. S. Bakheet and A. Al-Hamadi, Information, 7, 1 (2016).

    Article  Google Scholar 

  3. J. P. Wachs, M. Kölsch, H. Stern, and Y. Edan, Commun. ACM, 54, 60 (2011).

    Article  Google Scholar 

  4. O. Nierop, A. Helm, K. Overbeeke, and T. Djajadiningrat, Visual Computer, Springer, 24, 31 (2008).

  5. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Human action recognition via affine moment invariants,” in: The 21st International Conference on Pattern Recognition (ICPR-2012), p. 218.

  6. S. Siddharth and A. Rautaray, J. Artif. Intell. Res., Springer, 1 (2012).

  7. R. Azad, B. Azad, and I. T. Kazerooni, Adv. Comput. Sci., 2, 121 (2013).

    Google Scholar 

  8. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Cubic-splines neural network-based system for image retrieval,” in: Proceedings of the Sixth International IEEE Conference on Image Processing (ICIP-2009), p. 273.

  9. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, EURASIP J. Adv. Signal Process., 540375 (2011).

  10. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Towards robust human action retrieval in video,” in: Proceedings of the British Machine Vision Conference (BMVC-2010).

  11. P. Garg, N. Aggarwal, and S. Sofat, “Vision based hand gesture recognition,” in: World Academy of Science, Engineering and Technology (2009), p. 1.

  12. S. Mitra and T. Acharya, IEEE Trans. Syst. Man. Cybern. C: Appl. Rev., 37, 311 (2007).

    Article  Google Scholar 

  13. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Human activity recognition: A scheme using multiple cues,” in: Proceedings of the International Symposium on Visual Computing (ISVC-2010), Vol. 1, p. 574.

  14. A. Erol, G. Bebis, M. Nicolescu, et al., Comput. Vis. Image Underst., 108, 52 (2007).

    Article  Google Scholar 

  15. J. J. Kuch and T. S. Huang, “Human computer interaction via the human hand: a hand model,” in The 28th Asilomar Conference on Signals, Systems, and Computers (1994), p. 1252.

  16. M. Bray, E. Koller-Meier, and L. V. Gool, “Smart particle filtering for 3d hand tracking,” in: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (2004), p. 675.

  17. K. Nirei, H. Saito, M. Mochimaru, and S. Ozawa, “Human hand tracking from binocular image sequences,” in: The 22th International Conference on Industrial Electronics, Control, and Instrumentation (1996), p. 297.

  18. B. Stenger, A. Thayananthan, P. Torr, and R. Cipolla, EEE Trans. Pattern Anal. Machine Intell., 28, 1372 (2006).

    Article  Google Scholar 

  19. M. de La Gorce, N. Paragios, and D. J. Fleet, “Model-based hand tracking with texture, shading and self-occlusions,” in: IEEE Conference on Computer Vision and Pattern Recognition (2008), p. 1.

  20. M. de La Gorce, D. J. Fleet, and N. Paragios, IEEE Trans. Pattern Anal. Machine Intell., 33, no. 9, 1793 (2011).

    Article  Google Scholar 

  21. C. Kerdvibulvech and H. Saito, EURASIP J. Image Video Process., 724947 (2009).

  22. M. H. Yang, N. Ahuja, and M. Tabb, PAMI, 29, 1062 (2002).

    Google Scholar 

  23. C.-C. Wang and K.-C. Wang, “Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction,” Springer, Berlin/Heidelberg (2008), p. 317.

    Google Scholar 

  24. S. Bakheet and A. Al-Hamadi, Br. J. Math. Comput. Sci., 17, 1 (2016).

    Article  Google Scholar 

  25. J. Kovac, P. Peer, and F. Solina, “Human skin color clustering for face detection,” in: Proceedings of EUROCON (2003), p. 144.

  26. M. A. Mofaddel and S. Sadek, “Adult image content filtering: A statistical method based on multicolor skin modeling,” in: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT-2010), p. 366.

  27. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “A robust neural system for objectionable image recognition,” in: Proceedings of the Second International Conference on Machine Vision (ICMV-2009), p. 32.

  28. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, Int. J. Intell. Sci. (IJIS), 2, 9 (2012).

    Article  Google Scholar 

  29. S. Suzuki and K. Abe, CVGIP, 30, 32 (1985).

    Google Scholar 

  30. S. Sadek, A. Al-Hamadi, G. Krell, and B. Michaelis, ISRN J. Machine Vis., 1, 1 (2013).

    Article  Google Scholar 

  31. S. Sadek, A. Al-Hamadi, and B. Michaelis, WSEAS Trans. Inform. Sci. Appl., 10, 116 (2013).

    Google Scholar 

  32. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, ISRN J. Machine Vis., 1, 1 (2012).

    Article  Google Scholar 

  33. V. N. Vapnik, IEEE Trans. Neural Networks, 10, 988 (1999).

    Article  Google Scholar 

  34. C. Cortes and V. Vapnik, Machine Learning, 20, 1 (1995).

    Google Scholar 

  35. H.-K. Lee and J. Kim, IEEE Trans. Pattern Anal. Machine Intell., 21, 961 (1999).

    Article  Google Scholar 

  36. H.-D. Yang, S. Sclaroff, and S.-W. Lee, IEEE Trans. Pattern Anal. Machine Intell., 31, 1264 (2009).

    Article  Google Scholar 

  37. M. Elmezain, A. Al-Hamadi, S. Sadek, and B. Michaelis, “Robust methods for hand gesture spotting and recognition using hidden markov models and conditional random fields,” in: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT-2010), p. 131.

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science Faculty of Science, Sohag University, 82524, Sohag, Egypt

    Samy Bakheet

  2. Institute for Information Technology and Communications (IIKT), Otto-von-Guericke-Universität Magdeburg, Magdeburg, 39106, Germany

    Samy Bakheet

Authors
  1. Samy Bakheet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samy Bakheet.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bakheet, S. A Fuzzy Framework for Real-Time Gesture Spotting and Recognition. J Russ Laser Res 38, 61–75 (2017). https://doi.org/10.1007/s10946-017-9620-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10946-017-9620-1

Keywords

Search

Navigation