3D Gesture Recognition by Superquadrics

Ilya Afanasyev, Mariolino De Cecco


This paper presents 3D gesture recognition and localization method based on processing 3D data of hands in color gloves acquired by 3D depth sensor, like Microsoft Kinect. RGB information of every 3D datapoints is used to segment 3D point cloud into 12 parts (a forearm, a palm and 10 for fingers). The object (a hand with fingers) should be a-priori known and anthropometrically modeled by SuperQuadrics (SQ) with certain scaling and shape parameters. The gesture (pose) is estimated hierarchically by RANSAC-object search with a least square fitting the segments of 3D point cloud to corresponding SQ-models: at first – a pose of the hand (forearm & palm), and then positions of fingers. The solution is verified by evaluating the matching score, i.e. the number of inliers corresponding to the appropriate distances from SQ surfaces and 3D datapoints, which are satisfied to an assigned distance threshold.


  1. Afanasyev I., Lunardelli M., De Cecco M., et al. 2012. 3D Human Body Pose Estimation by Superquadrics. In Conf. Proc. VISAPP'2012 (Rome, Italy), V.2, 294-302.
  2. Burrus N. Kinect software “Skanect-0.1”. 2011. http://manctl.com/products.html.
  3. Heap A.J. and Hogg D.C., 1996. Towards 3-D hand tracking using a deformable model. In Conf. Proc. on Face and Gesture Recognition, P.140-145.
  4. Geebelen G., Cuypers T., Maesen S., and Bekaert P., 2010. Real-time hand tracking with a colored glove. In Conf. Proc. 3D Stereo Media.
  5. Jaklic A., Leonardis A., Solina F., 2000. Segmentation and Recovery of Superquadrics. Computational imaging and vision 20, Kluwer, Dordrecht.
  6. La Gorce M., Paragios N., Fleet D., 2008. Model-Based Hand Tracking with Texture, Shading and Selfocclusions. In IEEE Conf. Proc. CVPR. P.1-8.
  7. Leonardis A., Jaklic A., Solina F., 1997. Superquadrics for Segmenting and Modeling Range Data. In IEEE Conf. Proc. PAMI-19 (11). P. 1289-1295.
  8. Rehg J.M. and Kanade T., 1995. Model-based tracking of self-occluding articulated objects. In IEEE Conf. Proc. on Computer Vision, P. 612-617.
  9. Solina F. and Bajcsy R., 1990. Recovery of parametric models from range images: The case for superquadrics with global deformations. IEEE Transactions PAMI12(2):131-147.
  10. Stenger B., Mendonca P.R.S., and Cipolla R., 2001. Model-based 3D tracking of an articulated hand. In IEEE Conf. Proc. CVPR 2001 (2): 310-315.
  11. Starner T. and Pentland A., 1995. Real-time american sign language recognition from video using hidden Markov models. In IEEE Proc. Computer Vision, P. 265-270.
  12. Wang R.Y. and Popovic J., 2009. Real-time hand-tracking with a color glove. ACM Transactions on Graphics (TOG), 28 (3), 63.
  13. Zhou H. and Huang T. S., 2003. Tracking articulated hand motion with eigen dynamics analysis. In IEEE Conf. Proc. on Computer Vision, V. 2, P. 1102-1109.
  14. Figure 5: 3D gesture recognition by Superquadrics.

Paper Citation

in Harvard Style

Afanasyev I. and De Cecco M. (2013). 3D Gesture Recognition by Superquadrics . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 429-433. DOI: 10.5220/0004348404290433

in Bibtex Style

author={Ilya Afanasyev and Mariolino De Cecco},
title={3D Gesture Recognition by Superquadrics},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - 3D Gesture Recognition by Superquadrics
SN - 978-989-8565-48-8
AU - Afanasyev I.
AU - De Cecco M.
PY - 2013
SP - 429
EP - 433
DO - 10.5220/0004348404290433