Visual Hand Posture Recognition in Monocular Image Sequences

  • Thorsten Dick
  • Jörg Zieren
  • Karl-Friedrich Kraiss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


We present a model-based method for hand posture recognition in monocular image sequences that measures joint angles, viewing angle, and position in space. Visual markers in form of a colored cotton glove are used to extract descriptive and stable 2D features. Searching a synthetically generated database of 2.6 million entries, each consisting of 3D hand posture parameters and the corresponding 2D features, yields several candidate postures per frame. This ambiguity is resolved by exploiting temporal continuity between successive frames. The method is robust to noise, can be used from any viewing angle, and places no constraints on the hand posture. Self-occlusion of any number of markers is handled. It requires no initialization and retrospectively corrects posture errors when accordant information becomes available. Besides a qualitative evaluation on real images, a quantitative performance measurement using a large amount of synthetic input data featuring various degrees of noise shows the effectiveness of the approach.


Input Image View Angle Hand Model Monocular Camera Posture Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zieren, J., Kraiss, K.-F.: Robust Person-Independent Visual Sign Language Recognition. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 520–528. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Zieren, J.: Hand Gesture Commands. In: Advanced Man-Machine Interaction, pp. 7–56. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Bebis, G., Harris, F., Erol, A., Yi, B., Martinez, J., Hernandez-Usabiaga, J., Fritzinger, S.: Development of a Nationally Competitive Program in Computer Vision Technologies for Effective Human-Computer Interaction in Virtual Environments. Technical report, BioVIS Lab. In: BioVIS Technology Center of NASA Ames Research Center (2002)Google Scholar
  4. 4.
    Nölker, C.: Grefit: Ein System zur Visuellen Erkennung von Handposturen. PhD thesis, Technische Fakultät der Universität Bielefeld (2000)Google Scholar
  5. 5.
    Rehg, J.M.: Visual Analysis of High DOF Articulated Objects with Application to Hand Tracking. PhD thesis, School of Computer Science, Carnegie Mellon University (1995)Google Scholar
  6. 6.
    Imai, A., Shimada, N., Shirai, Y.: 3-D Hand Posture Recognition by Training Contour Variation. In: International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  7. 7.
    Vittrup, M., Sørensen, M.K.D., McCane, B.: Pose Estimation by Applied Numerical Techniques. In: Image and Vision Computing, New Zealand (2002)Google Scholar
  8. 8.
    Athitsos, V., Sclaroff, S.: Estimating 3D Hand Pose from a Cluttered Image. In: Proc. IEEE CVPR (2003)Google Scholar
  9. 9.
    Fillbrandt, H., Akyol, S., Kraiss, K.F.: Extraction of 3D Hand Shape and Posture from Image Sequences for Sign Language Recognition. In: Azada, D. (ed.) IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG) (2003)Google Scholar
  10. 10.
    Chua, C.S., Guan, H., Ho, Y.K.: Model-based 3D hand posture estimation from a single 2D image. Image Vision Comput. 20(3) (2002)Google Scholar
  11. 11.
    Lathuilière, F., Hervé, J.Y.: Visual Tracking of Hand Posture in a Robot Control Application. In: Proceedings of the Vision Interface Conference (1999)Google Scholar
  12. 12.
    Heap, T., Hogg, D.: Towards 3D Hand Tracking using a Deformable Model. In: International Conference on Automatic Face and Gesture Recognition (1996)Google Scholar
  13. 13.
    Holden, E.J., Owens, R., Roy, G.G.: 3D Hand Tracker for Visual Sign Recognition (1999)Google Scholar
  14. 14.
    Sturman, D.J.: Whole-hand Input. PhD thesis, School of Architecture and Planning, Massachusetts Institute of Technology (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thorsten Dick
    • 1
  • Jörg Zieren
    • 1
  • Karl-Friedrich Kraiss
    • 1
  1. 1.Institute of Man-Machine-InteractionRWTH Aachen UniversityGermany

Personalised recommendations