Geometric magnification-based attention graph convolutional network for skeleton-based micro-gesture recognition

Micro-Gesture (MG) recognition is an emerging and challenging task due to the short duration and small amplitude of joints. MGs indicate subtle movements of the body in response to stress, which are more difficult to recognize than regular gestures. To solve the above problems, for the modeling of micro-gesture skeleton data, we propose a Geometric Magnification-Based Attention Graph Convolutional Network (MA-GCN) to magnify and select features. The network mainly consists of two modules: the geometric magnification module (GM module) controls the magnification of different joints, and the spatial temporal attention graph convolution module (STA module) selects valid information by weighting different joints and frames to focus on subtle movements. Extensive experiments on two MG datasets prove that our method achieves remarkable performance.