Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition

Husam Al-Behadili, Arne Grumpe, Christian Wöhler


The automatic recognition of gestures is important in a variety of applications, e.g. human-machine-interaction. Commonly, different individuals execute gestures in a slightly different manner and thus a fully labelled dataset is not available while unlabelled data may be acquired from an on-line stream. Consequently, gesture recognition systems should be able to be trained in a semi-supervised learning scenario. Additionally, real-time systems and large-scale data require a dimensionality reduction of the data to reduce the processing time. This is commonly achieved by linear subspace projections. Most of the gesture data sets, however, are non-linearly distributed. Hence, linear sub-space projection fails to separate the classes. We propose an extension to linear subspace projection by applying a non-linear transformation to a space of higher dimensional after the linear subspace projection. This mapping, however, is not explicitly evaluated but implicitly used by a kernel function. The kernel nearest class mean (KNCM) classifier is shown to handle the non-linearity as well as the semi-supervised learning scenario. The computational expense of the non-linear kernel function is compensated by the dimensionality reduction of the previous linear subspace projection. The method is applied to a gesture dataset comprised of 3D trajectories. The trajectories were acquired using the Kinect sensor. The results of the semi-supervised learning show high accuracies that approach the accuracy of a fully supervised scenario already for small dimensions of the subspace and small training sets. The accuracy of the semi-supervised KNCM exceeds the accuracy of the original nearest class mean classifier in all cases.


  1. Al-Behadili, H., Wöhler, C., and Grumpe, A. (2014). Semisupervised learning of emblematic gestures. ATAUTOMATISIERUNGSTECHNIK, 62(10):732-739.
  2. Al-Behadili, H., Wöhler, C., and Grumpe, A. (2015). NonLinear Distance Based Large Scale Data Classifications. In 3'rd International conference on image Information Processing (ICIIP), page In Press. IEEE.
  3. Altman, N. S. (1992). An introduction to kernel and nearestneighbor nonparametric regression. The American Statistician, 46(3):175-185.
  4. Bhuyan, M., Bora, P., and Ghosh, D. (2008). Trajectory guided recognition of hand gestures having only global motions. International Journal of Computer Science, Fall.
  5. Boiman, O., Shechtman, E., and Irani, M. (2008). In defense of nearest-neighbor based image classification.
  6. In Computer Vision and Pattern Recognition, 2008.
  7. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  8. Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21-27.
  9. Elmezain, M., Al-Hamadi, A., Rashid, O., and Michaelis, B. (2009). Posture and Gesture Recognition for Human-Computer Interaction. In Jayanthakumaran, K., editor, Advanced Technologies, pages 415-440. InTech, Rijeka, Croatia.
  10. Fothergill, S., Mentis, H., Kohli, P., and Nowozin, S. (2012). Instructing people for training gestural interactive systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1737-1746. ACM.
  11. Guillaumin, M., Mensink, T., Verbeek, J., and Schmid, C. (2009). Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In Computer Vision, 2009 IEEE 12th International Conference on, pages 309-316. IEEE.
  12. Kung, S. Y. (2014). Kernel Methods and Machine Learning. Cambridge University Press.
  13. Mensink, T., Verbeek, J., Perronnin, F., and Csurka, G. (2013a). Distance-based image classification: Generalizing to new classes at near-zero cost. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11):2624-2637.
  14. Mensink, T., Verbeek, J., Perronnin, F., and Csurka, G. (2013b). Large scale metric learning for distancebased image classification on open ended data sets. In Advanced Topics in Computer Vision, pages 243-276. Springer.
  15. Richarz, J. and Fink, G. A. (2011). Visual recognition of 3d emblematic gestures in an hmm framework. Journal of Ambient Intelligence and Smart Environments, 3(3):193-211.
  16. Schneider, P., Biehl, M., and Hammer, B. (2009). Adaptive relevance matrices in learning vector quantization. Neural Computation, 21(12):3532-3561.
  17. Theodoridis, S., Pikrakis, A., Koutroumbas, K., and Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach. Academic Press.
  18. Webb, A. R. (2003). Statistical pattern recognition. John Wiley & Sons.
  19. Yoon, H.-S., Soh, J., Bae, Y. J., and Yang, H. S. (2001). Hand gesture recognition using combined features of location, angle and velocity. Pattern recognition, 34(7):1491-1501.
  20. Zhu, X. and Goldberg, A. B. (2009). Introduction to Semisupervised Learning. Synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool Publishers.

Paper Citation

in Harvard Style

Al-Behadili H., Grumpe A. and Wöhler C. (2016). Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 280-286. DOI: 10.5220/0005674102800286

in Bibtex Style

author={Husam Al-Behadili and Arne Grumpe and Christian Wöhler},
title={Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},

in EndNote Style

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition
SN - 978-989-758-175-5
AU - Al-Behadili H.
AU - Grumpe A.
AU - Wöhler C.
PY - 2016
SP - 280
EP - 286
DO - 10.5220/0005674102800286