An End-to-end Framework for Few-shot Millimeter-wave Radar-based Hand Gesture Recognition

Gesture recognition in few-shot scenarios presents a significant challenge due to the scarcity of labeled data. In this work, we propose a novel end-to-end framework tailored for few-shot gesture recognition, addressing the limitations of current methods. A novel feature map generating method is proposed to leverage a greater number of dimensions in capturing gesture feature information and simplify the structure of network. Our approach also maximizes the utility of a limited set of real training samples by generating new virtual samples in two domains based on data augmentation, and employs a feature fusion strategy for comprehensive gesture characteristic extraction by using both Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) to extract features. Extensive experimental results validate the efficacy of our proposed method, achieving a final accuracy of 85.26%, exhibiting a remarkable 35% improvement over the baseline, thereby confirming the effectiveness of our work in the challenging few-shot gesture recognition task.