Hand pose estimation using voxel-based individualized hand model

A robust and adaptive hand pose estimation technique is necessary to create a natural and non-contact manmachine interface system. In this regard, individualizing the system´s hand model is key to improving hand pose estimation. This paper addresses the issue of calibrating the hand model of the system for every user in order to get a more robust pose estimation results. Our system is a vision-based model-based approach that uses a skeletal hand model composed of a finger link structure and a surface structure. The surface structure is composed of voxel which is derived from silhouette images obtained by multi-viewpoint cameras. The finger link structure is estimated by searching for the optimum lengths from a set of values generated from the calibration motion of the fingers. We compared the output of using a standard (non-calibrated) hand model with our proposed individualized hand model in the pose estimation system. Results show that using the calibrated hand model has a more robust result than the one using the standard model.