Hand pose estimation using voxel-based individualized hand model

Abstract

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.