This thesis deals with the problem of estimating and tracking the full articulation of

human hands. Algorithmically recovering hand articulations is a challenging problem

due to the hand’s high number of degrees of freedom and the complexity of its

motions. Besides the accuracy and efficiency of the hand posture estimation, hand

tracking methods are faced with issues such as invasiveness, ease of deployment

and sensor artifacts. In this thesis several different hand tracking approaches are examined,

including marker-based optical motion capture, data-driven discriminative

visual tracking and generative tracking based on articulated registration, and various

contributions to these areas are presented. The problem of optimally placing reduced

marker sets on a performer’s hand for optical hand motion capture is explored. A

method is proposed that automatically generates functional reduced marker layouts

by optimizing for their numerical stability and geometric feasibility. A data-driven

discriminative tracking approach based on matching the hand’s appearance in the

sensor data with an image database is investigated. In addition to an efficient nearest

neighbor search for images, a combination of discriminative initialization and

generative refinement is employed. The method’s applicability is demonstrated in

interactive robot teleoperation. Various real human hand motions are captured and

statistically analyzed to derive low-dimensional representations of hand articulations.

An adaptive hand posture subspace concept is developed and integrated into a generative

real-time hand tracking approach that aligns a virtual hand model with sensor

point clouds based on constrained inverse kinematics. Generative hand tracking is

formulated as a regularized articulated registration process, in which geometrical

model fitting is combined with statistical, kinematic and temporal regularization

priors. A registration concept that combines 2D and 3D alignment and explicitly accounts

for occlusions and visibility constraints is devised. High-quality, non-invasive,

real-time hand tracking is achieved based on this regularized articulated registration

formulation.