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 ℓ1−2 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 ℓ1−2 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.
@article{arxiv.2104.14349,
title = {Hand Gesture Recognition Based on a Nonconvex Regularization},
author = {Jing Qin and Joshua Ashley and Biyun Xie},
journal= {arXiv preprint arXiv:2104.14349},
year = {2022}
}