Monocular Hand Pose Estimation Using Variable Metric Gradient-Descent

Abstract

In this paper, we propose a novel model-based approach to recover 3D hand pose from 2D images through a compact articulated 3D hand model whose parameters are inferred in a Bayesian manner. To this end, we propose generative models for hand and background pixels leading to a loglikelihood objective function which aims at enclosing hand-like pixels within the silhouette of the projected 3D model while excluding background-like pixels.Segmentation and hand pose estimation are unified through the minimization of a single likelihood function, which is novel and improve overall robustness. We derive the gradient in the hand parameter space of such an area-based objective function, which is new and allows faster convergence rate than gradient free methods. Furthermore , we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter is used to improve robustness through multiple hypotheses and to exploit temporal coherence. Very promising experimental results demonstrate the potentials of our approach.