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arXiv · 2104.14349

Hand Gesture Recognition Based on a Nonconvex Regularization

Jing Qin·Joshua Ashley·Biyun Xie
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Abstract

Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction. Sparse representation based methods have been widely used due to their efficiency and low demands on the training data. Recently, nonconvex regularization techniques including the $\ell_{1-2}$ regularization have been proposed in the image processing community to promote sparsity while achieving efficient performance. In this paper, we propose a vision-based hand gesture recognition model based on the $\ell_{1-2}$ regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have demonstrated the effectiveness of this method in identifying hand gestures.

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BibTeXRIS

Jing Qin, Joshua Ashley, Biyun Xie. 2021-04-29. Hand Gesture Recognition Based on a Nonconvex Regularization. https://doi.org/10.1109/icma52036.2021.9512752

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