  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.
