MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

Author:

Zheng Weiling1,Zhang Yu2,Jiang Landu3,Zhang Dian4,Gu Tao2

Affiliation:

1. School of Computing Technologies, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia

2. School of Computing, Macquarie University, 4 Research Park Drive, North Ryde, NSW 2109, Australia

3. Base of Red Bird MPhil, HKUST(GZ) University, No.1 Du Xue Rd., Guangzhou 511458, China

4. College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Blvd, Shenzhen 518060, China

Abstract

Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.

Funder

Stable Support Project of Shenzhen

Publisher

MDPI AG

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