Machine Vision and Applications

, Volume 25, Issue 5, pp 1309–1315 | Cite as

A synthetic training framework for providing gesture scalability to 2.5D pose-based hand gesture recognition systems

  • Javier MolinaEmail author
  • José M. Martínez
Original Paper


The use of hand gestures offers an alternative to the commonly used human computer interfaces (i.e., keyboard, mouse, gamepad), providing a more intuitive way of navigating among menus and in multimedia applications. One of the most difficult issues when designing a hand gesture recognition system is to introduce new detectable gestures without high cost, this is known as gesture scalability. Commonly, the introduction of new gestures needs a recording session of them, involving real subjects in the process. This paper presents a training framework for hand posture detection systems based on a learning scheme fed with synthetically generated range images. Different configurations of a 3D hand model result in sets of synthetic subjects, which have shown good performance in the separation of gestures from several dictionaries of the State of Art. The proposed approach allows the learning of new dictionaries with no need of recording real subjects, so it is fully scalable in terms of gestures. The obtained accuracy rates for the dictionaries evaluated are comparable to, and for some cases better than, the ones reported for different real subjects training schemes.


Human–computer interaction Hand gesture recognition Gesture scalability Hand model 


  1. 1.
    Laviola, J.J.: Bringing vr and spatial 3d interaction to the masses through video games. IEEE Comput. Graph. Appl. 28(5), 10–15 (2008) Google Scholar
  2. 2.
    Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(3), 311–324 (2007)CrossRefGoogle Scholar
  3. 3.
    Holte, M.B., Stoerring, M.: Pointing and command gestures under mixed illumination conditions: video sequence dataset. (2004)
  4. 4.
    Martin Larsson, D.K.: Isabel Serrano Vicente, Cvap arm/hand activity database. (2011)
  5. 5.
    Ho, M.-F., Tseng, C.-Y., Lien, C.-C., Huang, C.-L.: A multi-view vision-based hand motion capturing system. Pattern Recognit. 44, 443–453 (2011)CrossRefzbMATHGoogle Scholar
  6. 6.
    Causo, A., Matsuo, M., Ueda, E., Takemura, K., Matsumoto, Y., Takamatsu, J., Ogasawara, T.: Hand pose estimation using voxel-based individualized hand model. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 451–456 (2009)Google Scholar
  7. 7.
    Causo, A., Ueda, E., Kurita, Y., Matsumoto, Y., Ogasawara, T.: Model-based hand pose estimation using multiple viewpoint silhouette images and unscented kalman filter. In: The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp. 291–296 (2008)Google Scholar
  8. 8.
    Soutschek, S., Penne, J., Hornegger, J., Kornhuber, J.: 3D gesture-based scene navigation in medical imaging applications using time-of-flight cameras. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)Google Scholar
  9. 9.
    Kollorz, E., Penne, J., Hornegger, J., Barke, A.: Gesture recognition with a time-of-flight camera. Int. J. Intell. Syst. Technol. Appl. 5(3/4), 334–343 (2008)Google Scholar
  10. 10.
    Molina, J., Escudero-Vi nolo, M., Signoriello, A., Pardás, M., Ferrán, C., Bescós, J., Marqués, F., Martínez, J.M.: Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models. Mach. Vis. Appl. 1, 187–204 (2013)Google Scholar
  11. 11.
    Liu, X., Fujimura, K.: Hand gesture recognition using depth data. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 529–534 (2004)Google Scholar
  12. 12.
    Molina, J., Pajuelo, J.A., Escudero-Vi nolo, M., Bescós, J., Martínez, J.M.: A natural and synthetic corpus for benchmarking of hand gesture recognition systems. Mach. Vis. Appl. 25(4), 943–954 (2014)CrossRefGoogle Scholar
  13. 13.
    Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Estimating 3D hand pose using hierarchical multi-label classification. Image Vis. Comput. 25(12), 1885–1894 (2007). The age of human computer interactionCrossRefGoogle Scholar
  14. 14.
    Baysal, C.: Implementation of fuzzy similarity methods for manipulative hand posture evaluation. In: IEEE International Conference on Systems Man and Cybernetics, pp. 1320–1324 (2010)Google Scholar
  15. 15.
    Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: Vision-based hand pose estimation: a review. Comput. Vis. Image Underst. 108(1–2), 52–73 (2007)CrossRefGoogle Scholar
  16. 16.
    Ge, S., Yang, Y., Lee, T.: Hand gesture recognition and tracking based on distributed locally linear embedding. In: IEEE Conference on Robotics, Automation and Mechatronics, pp. 1–6 (2006)Google Scholar
  17. 17.
    Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Video Processing and Understanding Lab Laboratorio C-111, Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain
  2. 2.Ciudad Universitaria de CantoblancoMadridSpain

Personalised recommendations