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Gesture recognition is one of the most intuitive ways of interaction and hasgathered particular attention for human computer interaction. Radar sensorspossess multiple intrinsic properties, such as their ability to work in lowillumination, harsh weather conditions, and being low-cost and compact, makingthem highly preferable for a gesture recognition solution. However, mostliterature work focuses on solutions with a limited range that is lower than ameter. We propose a novel architecture for a long-range (1m - 2m) gesturerecognition solution that leverages a point cloud-based cross-learning approachfrom camera point cloud to 60-GHz FMCW radar point cloud, which allows learningbetter representations while suppressing noise. We use a variant of DynamicGraph CNN (DGCNN) for the cross-learning, enabling us to model relationshipsbetween the points at a local and global level and to model the temporaldynamics a Bi-LSTM network is employed. In the experimental results section, wedemonstrate our model's overall accuracy of 98.4% for five gestures and itsgeneralization capability.

Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition