One of the shortcomings of the existing model-based 3D hand tracking methods is the fact that they consider a fixed hand model, i.e., one with fixed shape parameters. In this work we propose an online model-based method that tackles jointly the hand pose tracking and the hand shape estimation problems. The hand pose is estimated using a hierarchical particle filter. The hand shape is estimated by fitting the shape model parameters over the observations in a frame history. The candidate shapes required by the fitting framework are obtained by optimizing the shape parameters independently in each frame. Extensive experiments demonstrate that the proposed method tracks the pose of the hand and estimates its shape parameters accurately, even under heavy noise and inaccurate shape initialization.
Video with experimental results
The electronic versions of the above publications can be downloaded from my publications page.