Hand Gesture Recognition Based on a Nonconvex Regularization
This is an incremental improvement for human-robot interaction, applying an existing regularization technique from image processing to hand gesture recognition.
The paper tackles hand gesture recognition for human-robot interaction by proposing a model based on nonconvex ℓ₁-₂ regularization, solved with ADMM, and demonstrates its effectiveness in numerical experiments on binary and grayscale datasets.
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.