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A Performance Evaluation of HMM and DTW for Gesture Recognition

  • Josep Maria Carmona
  • Joan Climent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

It is unclear whether Hidden Markov Models (HMMs) or Dynamic Time Warping (DTW) techniques are more appropriate for gesture recognition. In this paper, we compare both methods using different criteria, with the objective of determining the one with better performance. For this purpose we have created a set of recorded gestures. The dataset used includes many samples of ten different gestures, with their corresponding ground truth obtained with a kinect. The dataset is made public for benchmarking purposes.

The results show that DTW gives higher performance than HMMs, and strongly support the use of DTW.

Keywords

Hidden Markov Models Dynamic Time Warping Gesture Recognition Kinect 

References

  1. 1.
    Appenrodt, J., Elmezain, M., Al-Hamadi, A., Michaelis, B.: A hidden markov model-based isolated and meaningful hand gesture recognition. International Journal of Electrical, Computer, and Systems Engineering 3, 156–163 (2009)Google Scholar
  2. 2.
    ten Holt, G.A., Reinders, M.J.T., Hendriks, E.A.: Multi-Dimensional Dynamic Time Warping for Gesture Recognition. In: Thirteenth Annual Conference of the Advanced School for Computing and Imaging (2007)Google Scholar
  3. 3.
    Lee, H., Kim, J.: An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 21(10), 961–973 (1999)CrossRefGoogle Scholar
  4. 4.
    Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human computer interaction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 677–695 (1997)CrossRefGoogle Scholar
  5. 5.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE 77, 257–286 (1989)CrossRefGoogle Scholar
  6. 6.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Processing 26(1), 43–49 (1978)zbMATHCrossRefGoogle Scholar
  7. 7.
    Wexelblat, A.: An approach to natural gesture in virtual environments. ACM Transactions on Computer-Human Interaction 2(3), 179–200 (1995)CrossRefGoogle Scholar
  8. 8.
    Wilson, A.D., Bobick, A.F.: Parametric hidden Markov models for gesture recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 21(9), 884–900 (1999)CrossRefGoogle Scholar
  9. 9.
    Yang, M.-H., Ahuja, N.: Recognizing Hand Gestures Using Motion Trajectories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 466–472 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Josep Maria Carmona
    • 1
  • Joan Climent
    • 1
  1. 1.Barcelona Tech (UPC)Spain

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