Skip to main content

Hand Gesture Recognition Based on Segmented Singular Value Decomposition

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6277))

Abstract

The increasing interest in gesture recognition is inspired largely by creating a system which can identify specific human gestures and using gestures to convey information or control devices. In this paper we present a novel approach for recognizing hand gestures. The proposed approach is based on segmented singular value decomposition(SegSVD) and considers both local and global information regarding gesture data. In this approach, first singular vectors and singular values are evaluated together to define the similarity of two gestures. Experiments with hand gesture data prove that our approach can recognize gestures with high accuracy.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ishikawa, M., Matsumura, H.: Recognition of a hand-gesture based on self-organization using a dataglove. In: Proceedings of 6th International Conference on Neural Information Processing, ICONIP 1999, vol. 2 (1999)

    Google Scholar 

  2. Fels, S.S., Hinton, G.E.: Glove-talkii-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Transactions on Neural Networks 9(1), 205–212 (1998)

    Article  Google Scholar 

  3. Yang, K., Shahabi, C.: A pca-based kernel for kernel pca on multivariate time series. In: Proceedings of ICDM 2005 Workshop on Temporal Data Mining: Algorithms, Theory and Applications, New Orleans, Louisiana, USA, pp. 149–156 (November 2005)

    Google Scholar 

  4. Agnes Just, S.M.: A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition. Comput. Vis. Image Underst. 113(4), 532–543 (2009)

    Article  Google Scholar 

  5. Erol, A., Bebis, G., Nicolescu, M., Boyle, R., Twombly, X.: Vision-based hand pose estimation: A review. Computer Vision and Image Understanding 108(1-2), 52–73 (2007)

    Article  Google Scholar 

  6. Takahashi, T., Kishino, F.: Hand gesture coding based on experiments using a hand gesture interface device. ACM SIGCHI Bulletin 23(2), 67–74 (1991)

    Article  Google Scholar 

  7. Kadous, M.W.: Machine recognition of auslan signs using powergloves: Towards large-lexicon recognition of sign language. In: Proceedings of the Workshop on the Integration of Gesture in Language and Speech, pp. 165–174 (1996)

    Google Scholar 

  8. Kadous, M.: Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. PhD thesis (2002)

    Google Scholar 

  9. Parvini, F., Shahabi, C.: Utilizing bio-mechanical characteristics for user-independent gesture recognition. In: 21st International Conference on Data Engineering Workshops, Tokyo, p. 1170 (April 2005)

    Google Scholar 

  10. Eisenstein, J., Ghandeharizadeh, S., Golubchik, L., Shahabi, C., Yan, D., Zimmermann, R.: Device independence and extensibility in gesture recognition. In: IEEE Virtual Reality Conference (VR), LA, Los Angeles, CA, USA, pp. 207–214 (March 2003)

    Google Scholar 

  11. Jiayang, L., Zhen, W., Lin, Z., Wickramasuriya, J., Vasudevan, V.: uwave: Accelerometer-based personalized gesture recognition and its applications, pp. 1–9 (March 2009)

    Google Scholar 

  12. Yang, K., Shahabi, C.: A pca-based similarity measure for multivariate time series. In: Proceedings of the 2nd ACM International Workshop on Multimedia Databases, Washington, DC, USA, pp. 65–74 (November 2004)

    Google Scholar 

  13. Li, C., Zhai, P., Zheng, S., Prabhakaran, B.: Segmentation and recognition of multi-attribute motion sequences. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, New York, USA, pp. 836–843 (October 2004)

    Google Scholar 

  14. Li, C., Prabhakaran, B.: A similarity measure for motion stream segmentation and recognition. In: Proceedings of the 6th International Workshop on Multimedia Data Mining: Mining Integrated Media and Complex Data, Chicago, pp. 89–94 (August 2005)

    Google Scholar 

  15. Li, C., Prabhakaran, B., Zheng, S.: Similarity measure for multi-attribute data. In: Proceedings of the 2005 IEEE International Conference on Acoustics, Speach, and Signal Processing (ICASSP), Philadelphia, USA (March 2005)

    Google Scholar 

  16. Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics Speech and Signal Processing 32(6), 1109–1121 (1984)

    Article  Google Scholar 

  17. Weng, X., Shen, J.: Classification of multivariate time series using two-dimensional singular value decomposition. Knowledge-Based Systems 21(7), 535–539 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, J., Kavakli, M. (2010). Hand Gesture Recognition Based on Segmented Singular Value Decomposition. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15390-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15390-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15389-1

  • Online ISBN: 978-3-642-15390-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics