Graph-based Deep Learning for Dynamic Hand Gesture Recognition

Abstract

The need for human-computer interaction in robotics, virtual and augmented reality, and sign language understanding has made hand gesture recognition an attractive research topic. Numerous methods have been proposed in recent years. This paper proposes a graph-based deep learning model that integrates the TCR-GC module for spatial modeling and the MB-TC module for temporal modeling. The CTR-GC extracts spatial features and updates the graph topologies. A shared topology is used as a generic prior for channels and then fine-tuned according to the distinct correlations. The correlations are calculated for every sample, capturing more intricate connections between vertices. Extensive experiments are implemented on the SHREC public dataset. The experimental results show that our proposed method performs better than existing methods on the SHREC dataset.