# Hand Gesture Recognition Based on a Nonconvex Regularization

arXiv: https://arxiv.org/abs/2104.14349 · HTML: https://mlsanity.com/paper/2104.14349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Improved Optical Flow for Gesture-based Human-robot Interaction](https://mlsanity.com/paper/1905.08685/) (2019)
- [A Novel Regularization Based on the Error Function for Sparse Recovery](https://mlsanity.com/paper/2007.02784/) (2020)
- [Comparing LBP, HOG and Deep Features for Classification of Histopathology Images](https://mlsanity.com/paper/1805.05837/) (2018)

## Abstract (arXiv)

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.