Hand Gesture Recognition Based on a Nonconvex Regularization
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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 ℓ_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 ℓ_1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have shown the effectiveness of this method in identifying hand gestures.
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