Real-Time 3D Hand Shape Estimation Based on Inverse Kinematics and Physical Constraints

  • Ryuji Fujiki
  • Daisaku Arita
  • Rin-ichiro Taniguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

We are researching for real-time hand shape estimation, which we are going to apply to user interface and interactive applications. We have employed a computer vision approach, since unwired sensing provides restriction-free observation, or a natural way of sensing. The problem is that since a human hand has many joints, it has geometrically high degrees of freedom, which makes hand shape estimation difficult. For example, we have to deal with a self-occlusion problem and a large amount of computation. At the same time, a human hand has several physical constraints, i.e., each joint has a movable range and interdependence, which can potentially reduce the search space of hand shape estimation. This paper proposes a novel method to estimate 3D hand shapes in real-time by using shape features acquired from camera images and physical hand constraints heuristically introduced. We have made preliminary experiments using multiple cameras under uncomplicated background. We show experimental results in order to verify the effectiveness of our proposed method.

Keywords

Joint Angle Inverse Kinematics Human Hand Hand Shape Wrist Position 
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.

References

  1. 1.
    Shimada, N., Shirai, Y.: Hand Posture Estimation based on 2D Appearance Retrieval Using Monocular Camera. In: Proc. Int, Workshop on RATFG-RTS, pp. 23–30 (2001)Google Scholar
  2. 2.
    Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Filtering using a tree-based estimater. In: Proc. ICCV, pp. 1063–1070 (2003)Google Scholar
  3. 3.
    Ueda, E., Matsumoto, Y., Imai, M., Ogasawara, T.: Hand Pose Estimation for Vision-based Human Interface. IEEE Trans. on Industrial Electronics. 50(4), 676–684 (2003)CrossRefGoogle Scholar
  4. 4.
    Lu, S., Metaxas, D., Samaras, D., Oliensis, J.: Using multiple cues for hand tracking and model refinement. In: Proc. CVPR, pp. 443–450 (2003)Google Scholar
  5. 5.
    Häger-Ross, C., Schieber, M.H.: Quantifying the Independence of Hand Finger Movements: Comparisons of Digit, Hands, nad Movement Frequencies. The Journal of Neuroscience 20(22), 8542–8550 (2000)Google Scholar
  6. 6.
    Kamper, D.G., Cruz, E.G., Siegel, M.P.: Stereotypical fingertip trajectories during grasp. Journal of Neurophysiology 90(6), 3702–3710 (2003)CrossRefGoogle Scholar
  7. 7.
    ElKoura, G., Singh, K.: Handrix: Animating the Human Hand. In: Proc. SIGGRAPH, pp. 110–119 (2003)Google Scholar
  8. 8.
    Wang, L.T., Chen, C.C.: A combined optimization method for solving the inverse kinematics problem of mechanical manipulators. IEEE Trans. on Robotics and Automations 17(4), 489–499 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ryuji Fujiki
    • 1
  • Daisaku Arita
    • 1
  • Rin-ichiro Taniguchi
    • 1
  1. 1.Department of Intelligent SystemsKyushu UniversityFukuokaJapan

Personalised recommendations