Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks
This work addresses gesture recognition for human-computer interaction, presenting an incremental improvement over existing methods.
The paper tackles hand gesture recognition from skeletal data by modeling hand skeletons as graphs and using neural networks to learn SPD matrices, achieving state-of-the-art performance on the Dynamic Hand Gesture dataset from SHREC 2017.
In this paper, we propose a new hand gesture recognition method based on skeletal data by learning SPD matrices with neural networks. We model the hand skeleton as a graph and introduce a neural network for SPD matrix learning, taking as input the 3D coordinates of hand joints. The proposed network is based on two newly designed layers that transform a set of SPD matrices into a SPD matrix. For gesture recognition, we train a linear SVM classifier using features extracted from our network. Experimental results on a challenging dataset (Dynamic Hand Gesture dataset from the SHREC 2017 3D Shape Retrieval Contest) show that the proposed method outperforms state-of-the-art methods.