GesturePrint: Enabling User Identification for mmWave-based Gesture Recognition Systems

TL;DR

GesturePrint is a novel system that combines gesture recognition and user identification using commodity mmWave radar, achieving high accuracy in recognizing gestures and identifying users with minimal additional cost.

Contribution

It introduces GesturePrint, the first system to enable both gesture recognition and user identification with mmWave radar using an attention-based neural network architecture.

Findings

Achieves 98.87% gesture recognition accuracy

Achieves 99.78% user identification accuracy in a meeting room

Outperforms existing methods on multiple datasets

Abstract

The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In this paper, we propose GesturePrint, which is the first to achieve gesture recognition and gesture-based user identification using a commodity mmWave radar sensor. GesturePrint features an effective pipeline that enables the gesture recognition system to identify users at a minor additional cost. By introducing an efficient signal preprocessing stage and a network architecture GesIDNet, which employs an attention-based multilevel feature fusion mechanism, GesturePrint effectively extracts unique gesture features for gesture recognition and personalized motion pattern features for user identification. We implement GesturePrint and collect data from 17 participants performing 15 gestures in a meeting room and an office, respectively. GesturePrint achieves a gesture recognition accuracy (GRA) of 98.87% with a user identification accuracy (UIA) of 99.78% in the meeting room, and 98.22% GRA with 99.26% UIA in the office. Extensive experiments on three public datasets and a new gesture dataset show GesturePrint's superior performance in enabling effective user identification for gesture recognition systems.