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

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...

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Vydáno v:Proceedings of the International Conference on Distributed Computing Systems s. 1074 - 1085
Hlavní autoři: Xu, Lilin, Wang, Keyi, Gu, Chaojie, Guo, Xiuzhen, He, Shibo, Chen, Jiming
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 23.07.2024
Témata:
Accuracy
Costs
Feature extraction
Gesture recognition
Millimeter-wave radar
Network architecture
Point cloud compression
RF sensing
Signal processing
User identification
ISSN:2575-8411
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Shrnutí: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 multi-level 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.
ISSN:2575-8411
DOI:10.1109/ICDCS60910.2024.00103