Clustering-Driven DGS-Based Micro-Doppler Feature Extrac-tion for Automatic Dynamic Hand Gesture Recognition

Previous study has pointed out that micro-Doppler signatures of hand gestures exhibit obvious sparseness in time-frequency domain. We introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures.

The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features.