The 3D hand skeletal data has received considerable amount of attention due to its potential uses case for Hand Gesture Recognition (HGR) systems. Additionaly, Graph Neural Network (GNN) have been widely employed for skeletal-based HGR. Yet still, traditional models frequently suffer from inefficient feature representation and generalizability. Thus, to overcome these limitations, we introduce Spatial Graph Isomorphism Neural Networks (S-GINs), which use GIN layers to improve the feature aggregation. First, we create a graph representing skeletal-based recordings. Following that, a spatial network convolution module learns the inherent topology of hand gestures from neighbor nodes and updates it using a multilayer perceptron. This method promotes classification accuracy over other spatial based graph models including graph attention network, graph sampeling and aggregating, and graph convolutional networks. We validate S-GINs employing three benchmark datasets: MMHGD, 2MLMD, and Multi-view Leap2 , both horizontal and vertical sub-datasets. Experimental findings show that our model outperforms state-of-the-art graph based methods, by elevating the accuracies to 72%, 81%, 85%, and 83% respectively .

Hand Gesture Recognition (HGR) achieved significant progress through diverse fields due to recent advancements in machine learning and sensor technologies. While Leap Motion Controller sensors offer convenient hand tracking and multi-modal data (skeletal and depth), the heterogeneous nature of these data modalities poses several challenges for HGR systems. In order to exploit the complementary information offered by skeleton and depth data, fusion algorithms are widely used. This paper proposes a novel Deep CNN-BiGRU model incorporating both intermediate and late fusion strategies. For each modality, we use a separate model for feature extraction step. Then, we apply fusion techniques for the decision step. Our proposed model demonstrates superior performance compared with models employed separately on skeletal or depth data, highlighting its effectiveness in exploiting the combined information for robust and accurate HGR.