User-Adaptable Hand Pose Estimation Technique for Human-Robot Interaction

Abstract

Hand pose estimation using a multi-camera system allows natural non-contact interfacing unlike when using bulky data gloves. To enable any user to use the system regardless of gender or physical differences such as hand size, we propose hand model individualization using only multiple cameras. From the calibration motion, our method estimates the finger link lengths as well as the hand shape by minimizing the gap between the hand model and observation. We confirmed the feasibility of our proposal by comparing 1) actual and estimated link lengths and 2) hand pose estimation results using our calibrated hand model, a prior hand model and data obtained from data glove measurements.