Hand action recognition is essential. Communication, human-robot interactions, and gesture control are dependent on it. Skeleton-based action recognition traditionally includes hands, which belong to the classes which remain challenging to correctly recognize to date. We propose a method specifically designed for hand action recognition which uses relative angular embeddings and local Spherical Harmonics to create novel hand representations. The use of Spherical Harmonics creates rotation-invariant representations which make hand action recognition even more robust against inter-subject differences and viewpoint changes. We conduct extensive experiments on the hand joints in the First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations, and on the NTU RGB+D 120 dataset, demonstrating the benefit of using Local Spherical Harmonics Representations. Our code is available at https://github.com/KathPra/LSHR_LSHT.

翻译：手部动作识别至关重要。通信、人机交互和手势控制都依赖于此。传统的基于骨骼的动作识别包含手部，但手部动作类别至今仍是难以准确识别的挑战之一。我们提出一种专用于手部动作识别的方法，该方法利用相对角度嵌入和局部球谐函数构建新型手部表征。球谐函数的使用创建了旋转不变的表征，使得手部动作识别在应对个体差异和视角变化时更具鲁棒性。我们在包含RGB-D视频和3D手部姿态标注的第一人称手部动作基准数据集以及NTU RGB+D 120数据集上对手部关节点进行了大量实验，证明了使用局部球谐函数表征的优越性。我们的代码已开源在 https://github.com/KathPra/LSHR_LSHT。