  Gesture recognition is one of the most intuitive ways of interaction and has
gathered particular attention for human computer interaction. Radar sensors
possess multiple intrinsic properties, such as their ability to work in low
illumination, harsh weather conditions, and being low-cost and compact, making
them highly preferable for a gesture recognition solution. However, most
literature work focuses on solutions with a limited range that is lower than a
meter. We propose a novel architecture for a long-range (1m - 2m) gesture
recognition solution that leverages a point cloud-based cross-learning approach
from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning
better representations while suppressing noise. We use a variant of Dynamic
Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships
between the points at a local and global level and to model the temporal
dynamics a Bi-LSTM network is employed. In the experimental results section, we
demonstrate our model's overall accuracy of 98.4% for five gestures and its
generalization capability.
