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3D Human Hand Posture Reconstruction Using a Single 2D Image
Mahdi Vaezi, Mohammad Ali Nekouie
Pages - 83 - 94     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 1   Issue - 4    |    Publication Date - January / February 2011  Table of Contents
3D hand posture estimation, machine vision, Gesture recognition, Model-based approach, color image
Passive sensing of the 3D geometric posture of the human hand has been studied extensively over the past decade. However, these research efforts have been hampered by the computational complexity caused by inverse kinematics and 3D reconstruction. In this paper, our objective focuses on 3D hand posture estimation based on a single 2D image with aim of robotic applications. We introduce the human hand model with 27 degrees of freedom (DOFs) and analyze some of its constraints to reduce the DOFs without any significant degradation of performance. A novel algorithm to estimate the 3D hand posture from eight 2D projected feature points is proposed. Experimental results using real images confirm that our algorithm gives good estimates of the 3D hand pose. Keywords: 3D hand posture estimation; Model-based approach; Gesture recognition; human- computer interface; machine vision.
CITED BY (3)  
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2 Tzionas, D., Srikantha, A., Aponte, P., & Gall, J. (2014). Capturing hand motion with an RGB-D sensor, fusing a generative model with salient points. In Pattern Recognition (pp. 277-289). Springer International Publishing.
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Mr. Mahdi Vaezi
science and research branch of islamic azad university - Iran
Dr. Mohammad Ali Nekouie
KNT technical university - Iran