Skip to main content
SpringerLink
Log in

Real-Time Hand Pose Recognition

  • Chapter
  • First Online:
Machine Learning for Audio, Image and Video Analysis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

  • 4634 Accesses

Abstract

What the reader should know to understand this chapter \(\bullet \) Color Models (Chap. 3). \(\bullet \) Learning Vector Quantization (Chap. 8).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Kinect is a registered trademark by Microsoft corp.

  2. 2.

    DG5 Vhand 2.0 is a registered trademark of DGTech Engineering Solutions.

  3. 3.

    The database is available on request.

  4. 4.

    The database is available on request.

  5. 5.

    Cyberglove is a registered trademark by Immersive Corp.

References

  1. O. Al-Jarrah and A. Halawani. Recognition of gesture in arabic sign language using neuro-fuzzy systems. Artificial Intelligence, 133(1–2):117–138, 2001.

    Google Scholar 

  2. M. Al-Roussan, K. Assaleh, and A. Talaa. Video-based signer independent arabic sign language recognition using hidden-markov models. Applied Soft Computing, 9:990–999, 2009.

    Google Scholar 

  3. O. Aran, T. Burger, A. Caplier, and L. Akarun. A belief-based sequential fusion approach for fusing manual signs and non-manual signals. Pattern Recognition, 42:812–822, 2009.

    Google Scholar 

  4. R.T. Azuma. A survey of augmented reality. Presence, 6(4):355–385, 1997.

    Google Scholar 

  5. R.T. Azuma, Y. Baillot, R. Behringer, S. Feiner, S. Julier, and B. MacIntyre. Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6):34–47, 2001.

    Google Scholar 

  6. B. Bauer and K.F. Kraiss. Video-based sign recognition using self-organizing subunits. In Proceedings of the 16th International Conference on Pattern Recognition, pages 434–437, 2002.

    Google Scholar 

  7. K. Bhuyan, D.R. Neog, and K.M. Kar. Fingertip detection for handpose recognition. International Journal on Computer Science and Engineering, 4(3):501–511, 2012.

    Google Scholar 

  8. M. Billinghurst. Put that where? voice and gesture at the graphics interface. SIGGRAPH Computer Graphics, 32(4):60–63, 1998.

    Google Scholar 

  9. G. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Cambridge (USA), 2008.

    Google Scholar 

  10. G.C. Burdea and P. Coiffet. Virtual Reality Technology. John-Wiley & Sons, New York, 2003.

    Google Scholar 

  11. F. Camastra and D. De Felice. A lvq-based hand gesture recognizer using a data glove. In Neural Nets and Surroundings, pages 159–168. Springer, 2012.

    Google Scholar 

  12. H.D. Cheng, X.H. Jiang, Y. Sun, and J. Wang. Color image segmentation: advances and prospects. Pattern Recognition, 34(12):2259–2281, 2001.

    Google Scholar 

  13. A. DelBimbo. Visual Information Processing. Morgan Kaufmann Publishers, San Francisco, 1999.

    Google Scholar 

  14. L. Dipietro, A.M. Sabatini, and P. Dario. A survey of glove-based systems and their applications. IEEE Transactions on Systems, Man and Cybernetics, 38(4):461–482, 2008.

    Google Scholar 

  15. G. Drew Kessler, L.F. Hodges, and N. Walker. Evaluation of the cyberglove as a whole-hand input device. ACM Transactions on Computer-Human Interaction, 2(4):263–283, 1995.

    Google Scholar 

  16. T. Drummond and R. Cipolla. Real-time visual tracking of complex structures. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(7):932–946, 2002.

    Google Scholar 

  17. A. Erol, G. Bebis, M. Nicolescu, R.D. Boyle, and X. Twombly. Vision-based hand pose estimation: A review. Computer Vision and Image Understanding, 108:52–73, 1998.

    Google Scholar 

  18. W. Gao, G.L. Fang, D.B. Zhao, and Y.Q.A. Chen. A chinese sign language recognition system based on sofm/srn/hmm. Pattern Recognition, 37:2389–2402, 2004.

    Google Scholar 

  19. R.C. Gonzales and R.E. Woods. Digital Image Processing. Prentice-Hall, Upper Saddle River, 2002.

    Google Scholar 

  20. M.-K. Hu. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2):179–187, 1962.

    Google Scholar 

  21. S.-I. Joo, S.-H. Weon, and H.-I. Choi. Real-time depth-based hand detection and tracking. The Scientific World Journal, pages 1–13, 2014.

    Google Scholar 

  22. A Kendon. How gestures can become like words. In Crosscultural perspectives in nonverbal communication, pages 131–141, Toronto, Hogrefe, 1988.

    Google Scholar 

  23. J.S. Kim, W. Jang, and Z. Bien. A dynamic gesture recognition system for the korean sign language (ksl). IEEE Transactions on Systems, Man and Cybernetics, Part B, 26:354–359, 1996.

    Google Scholar 

  24. T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, and K. Torkkola. Lvq-pak: The learning vector quantization program package. Technical Report A30, Helsinki University of Technology, Laboratory of Computer and Information Science, 1996.

    Google Scholar 

  25. L. Lamberti and F. Camastra. Real-time hand gesture recognition using a color glove. In Image Analysis and Processing - ICIAP 2011, pages 365–373. Springer, 2011.

    Google Scholar 

  26. L. Lamberti and F. Camastra. Handy: A real-time three color glove-based gesture recognizer with learning vector quantization. Expert Systems with Applications, 39:10489–10494, 2012.

    Google Scholar 

  27. X. Liu and K. Fujimura. Hand gesture recognition using depth data. In Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition, pages 529–534, 2004.

    Google Scholar 

  28. D.G. Lowe. Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5):441–450, 1991.

    Google Scholar 

  29. S. Malassiotis and M.G. Strintzis. Real-time hand posture recognition using range data. Image and Vision Computing, 26(7):1027–1037, 2008.

    Google Scholar 

  30. Z. Mo and U. Neumann. Real-time hand pose recognition using low-resolution depth images. In Proceedings of the 2006 IEEE Computer Society on Computer Vision and Pattern Recognition (CVPR’06), pages 1499–1505, 2006.

    Google Scholar 

  31. J. O’ Rourke and N.I. Badler. Model-based image analysis of human motion using constraint propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(6):522–536, 1980.

    Google Scholar 

  32. I. Oikonomidis, N. Kyriazis, and A.A. Argyros. Efficient model-based 3d tracking of hand articulations using kinect. In British Machine Vision Conference on Pattern Recognition, pages 101.1–101.11, 2011.

    Google Scholar 

  33. N. Otsu. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics, SMC-9(1):62–66, 2007.

    Google Scholar 

  34. M.S. Park, Md.M. Hasan, J.M. Kim, and O.S. Chae. Hand detection and tracking using depth and color information. In Proceedings of ICPV’12, pages 779–785, 2012.

    Google Scholar 

  35. D.L. Quam. Gesture recognition with a data glove. In IEEE National Aerospace and Electronic Conference, pages 755–760. IEEE, 1990.

    Google Scholar 

  36. F. Quek, D. McNeill, R. Bryll, S. Duncan, X.-F. Ma, C. Kirbas, K.E. McCullogh, and R. Ansari. Multimodal human discourse: gesture and speech. ACM Transactions on Computer-Human Interaction, 9(3):171–193, 2002.

    Google Scholar 

  37. F.K.H. Quek. Unencumbered gestural interaction. IEEE Multimedia, 3(4):36–47, 2007.

    Google Scholar 

  38. Y. Ren and C. Gu. Real-time hand gesture recognition based on vision. In Entertainment for Education, Digital Techniques and Systems, pages 468–475. Springer, 2010.

    Google Scholar 

  39. T. Starner, J. Weaver, and A. Pentland. Real-time american sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1371–1375, 1998.

    Google Scholar 

  40. H.I. Suk and B.H. Sin. Dynamic bayesian network based two-hand gesture recognition. Journal of KIISE: Software and Applications, 35(4), 2008.

    Google Scholar 

  41. P. Suryanarayan. Dynamic hand pose recognition using depth data. In 2010 International Conference on Pattern Recognition, pages 3105–3108, 2010.

    Google Scholar 

  42. P. Trinindade, J. Lobo, and J.P. Barreto. Hand gesture recognition using color and depth images enhanced with hand angular pose data. In Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages 71–76, 2012.

    Google Scholar 

  43. M. Van der Bergh and L. Van Gool. Combining rgb and tof cameras for real-time 3d hand gesture interaction. In Proceedings of 2011 IEEE Workshop on Application of Computer Vision (WACV), pages 66–72, 2011.

    Google Scholar 

  44. R.Y. Wang and J. Popovic. Real-time hand-tracking with a color glove. ACM Transactions on Graphics, 28(3):461–482, 2009.

    Google Scholar 

  45. A. Wexelblat. An approach to natural gesture in virtual environments. ACM Transactions on Computer-Human Interaction, 2(3):179–200, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Science and Technology, Parthenope University of Naples, Naples, Italy

    Francesco Camastra

  2. School of Computing Science and the Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK

    Alessandro Vinciarelli

Authors
  1. Francesco Camastra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandro Vinciarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Camastra .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag London

About this chapter

Cite this chapter

Camastra, F., Vinciarelli, A. (2015). Real-Time Hand Pose Recognition. In: Machine Learning for Audio, Image and Video Analysis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-6735-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6735-8_15

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6734-1

  • Online ISBN: 978-1-4471-6735-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation