Static Hand Gesture Recognition Based on Millimeter-Wave Near-Field FMCW-SAR Imaging

Abstract

To address the limitations of wireless sensing in static gesture recognition and the issues of Computer Vision’s dependence on lighting conditions, we propose a method that utilizes millimeter-wave near-field SAR (Synthetic Aperture Radar) imaging for static gesture recognition. First, a millimeter-wave near-field SAR imaging system is used to scan the defined static gestures to obtain data. Then, based on the distance plane, the three-dimensional gesture is divided into multiple two-dimensional planes, constructing an imaging dataset. Finally, an HOG (Histogram of Oriented Gradients) is used to extract features from the imaging results, PCA (Principal Component Analysis) is applied for feature dimensionality reduction, and RF (Random Forest) performs classification. Experimental verification shows that the proposed method achieves an average recognition precision of 97% in unobstructed situations and 93% in obstructed situations, providing an effective means for wireless-sensing-based static gesture recognition.