Hand modeling and tracking from voxel data: An integrated framework with automatic initialization

We propose an integrated framework for automated hand model initialization and tracking using voxel data. Starting with an initial specific hand pose, the Laplacian Eigenspace (LE) based segmentation method is applied to segment hand voxel into different parts. This segmentation result is then used to extend the kinematically constrained gaussian mixture model (KC-GMM) method for articulated body pose inference with an automated hand model initialization. Our experiment with both synthesized hand voxel and real hand voxel captured from multi-perspective thermal cameras show that by combining the two methods, we have a more powerful system than using each one solitarily.