mmGes: Coarse-Fine-Grained Feature Fusion for Gesture Recognition via Contact-Less mmWave Sensing

Millimeter wave radar has recently emerged as a promising modality for enabling pervasive gesture recognition while protecting user privacy. However, personalized user behaviors, interference from unexpected actions, and long-term variability in user gestures significantly degrade the accuracy of gesture and user identity recognition, thereby compromising the quality of user experiences. To this end, we designmmGeswith four key modules: (i) afine-grained feature extractorextracts micro-level features from the time-series radar data to identify users' personalized behaviors; (ii) auser-specific feature classifierextracts coarse-grained features from a global perspective, followed by analyzing the micro-details of gesture features to recognize the user; (iii) avoting-based multi-user recognizerretrieves all pre-trained models from the user model database, followed by obtaining the probability indicators of each to return recognition results; (iv) alifelong learning modelretains previous knowledge while adjusting its feature selection capabilities using newly collected data to adapt to gesture changes. We implement and evaluatemmGesusing three self-collected real-world radar datasets, demonstrating its superior performance compared to other state-of-the-art gesture recognition methods.