  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 requirements 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 human arm gesture recognition model based on the $\ell_{1-2}$
regularization, which is solved by the alternating direction method of
multipliers (ADMM). Numerical experiments on realistic data sets have shown the
effectiveness of this method in identifying arm gestures.
