Capturing articulated human hand motion: A divide-and-conquer approach

The use of human hand as a natural interface device serves as a motivating force for research in the modeling, analyzing and capturing of the motion of articulated hand. Model-based hand motion capturing can be formulated as a large nonlinear programming problem, but this approach is plagued by local minima. An alternative way is to use analysis-by-synthesis by searching a huge space, but the results are rough and the computation expensive. In this paper, articulated hand motion is decoupled, a new two-step iterative model-based algorithm is proposed to capture articulated human hand motion, and a proof of convergence of this iterative algorithm is also given. In our proposed work, the decoupled global hand motion and local finger motion are parameterized by 3D hand pose and the state of the hand respectively. Hand pose determination is formulated as a least median of squares (LMS) problem rather than the non-robust least squares (LS) problem, so that 3D hand pose can be reliably calculated even if there are outliers. Local finger motion is formulated as an inverse kinematics problem. A GA-based method is proposed to find a sub-optimal solution of inverse kinematics effectively. Our algorithm and the LS-based algorithm are compared in several experiments. Both algorithms converge when local finger motion between consecutive frames is small. When large finger motion is present, the LS-based method fails, but our algorithm can still estimate the global and local finger motion well.