Hand Gesture Recognition Using Radial and Transversal Dual Micromotion Features

Most of the work in hand gesture recognition (HGR) focuses on developing diverse classification algorithms based on micro-Doppler (mD) spectrogram, that is 1-D motion along the radial direction. In this work, we exert effort on the radar system and preprocessing methods to extract 2-D motions for HGR. Specifically, we utilize an interferometric radar with two widely spaced receivers to obtain both radial and transversal micromotion features of hand gestures. In the preprocessing stage, as pre-interferometry is nonlinear multiplication in time domain, both the increased noise level and unwanted cross-terms may reduce its usefulness for HGR. To solve these problems, we propose a post-interferometric preprocessing method in frequency domain, which is capable of reducing noise level of the obtained spectrogram and suppressing the nuisance cross-terms. We measure four pairs of symmetric hand gestures from three persons and compare the HGR accuracy using different preprocessing methods. Experimental results show that the mD processing combined with post-interferometry give the highest HGR accuracy of over 99%.