Abstract
This paper presents an approach to recognition of static hand gestures based on data acquired from 3D cameras and point cloud descriptors: Ensemble of Shape Functions and Global Radius-based Surface Descriptor. We describe the recognition algorithm consisting of: hand segmentation, noise removal and downsampling of point cloud, dividing point cloud bounding box to cells, feature extraction and normalization, gesture classification. Modifications of the descriptors are proposed in order to increase hand posture recognition rates and decrease quantity of used features as well as computational cost of the algorithm. The experiments performed on four challenging datasets using cross-validation tests prove the usefulness of our approach.
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