Normalized edge convolutional networks for skeleton-based hand gesture recognition

Highlights：

• We propose a novel edge-varying graph by dividing each neighborhood of the central node into three groups: physical neighbors, temporal neighbors and varying neighbors. The design trick design called a “black hole” is presented to enhance the performance of the graph.

• We conduct edge normalization within the central node's two-hop neighborhood, resulting in a novel normalized edge convolution operation.

• A novel sampling strategy called zig-zag sampling is proposed. The strategy is designed to maintain a graceful balance between intergroup and intragroup sampling priorities.

• Normalized edge convolutional networks are constructed for hand gesture recognition, and systematic experiments on publicly available datasets validate the robustness and superiority of our method.