Towards Multi-View Hand Pose Recognition Using a Fusion of Image Embeddings and Leap 2 Landmarks

This paper presents a novel approach for multi-view hand pose recognition through image embeddings and hand landmarks. The method integrates raw image data with structural hand landmarks derived from the Leap Motion Controller 2. A Vision Transformer (ViT) pretrained model was used to extract visual features from dual-view grayscale images, which were fused with the corresponding Leap 2 hand landmarks, creating a multimodal representation that encapsulates both visual and landmark data for each sample. These fused embeddings were then classified using a multi-layer perceptron to distinguish among 17 distinct hand poses from the Multi-view Leap2 Hand Pose Dataset, which includes data from 21 subjects. Using a Leave-OneSubject-Out Cross-Validation (LOSO-CV) strategy, we demonstrate that this fusion approach offers a robust recognition performance (F1 Score of 79.33 ± 0.09 %), particularly in scenarios where hand occlusions or challenging angles may limit the utility of single-modality data.