The recent successful commercialization of depth sensors has made it possible to effectively capture depth images in real time, and thus creates a new modality for many computer vision tasks including hand gesture recognition and activity analysis. Most existing depth descriptors simply encode depth information as intensities while ignoring the richer 3D shape information. In this paper, we propose a novel and effective descriptor, the Histogram of 3D Facets (H3DF), to explicitly encode the 3D shape information from depth maps. A 3D Facet associated with a 3D cloud point characterizes the 3D local support surface. By robust coding and circular pooling 3D Facets from a depth map, the proposed H3DF descriptor can effectively represent both 3D shapes and structures of various depth maps. To address the recognition problems of dynamic actions and gestures, we further extend the proposed H3DF by combining it with an N-gram model and dynamic programming. The proposed descriptor is extensively evaluated on two public 3D static hand gesture datasets, one dynamic hand gesture dataset, and one popular 3D action recognition dataset. The recognition results outperform or are comparable with state-of-the-art performances. 
Histogram of 3D Facets: A depth descriptor for human action and hand gesture recognition