TRGR: Transmissive RIS-aided Gait Recognition Through Walls

TL;DR

TRGR introduces a novel RF-based gait recognition system using transmissive RIS to accurately identify individuals through walls, overcoming LOS and SNR limitations with high accuracy.

Contribution

The paper presents TRGR, the first system leveraging transmissive RIS and a residual CNN for through-wall gait recognition with high accuracy.

Findings

Achieves 97.88% accuracy through concrete walls.

Utilizes transmissive RIS to improve signal quality and wall penetration.

Demonstrates robustness and effectiveness in experimental tests.

Abstract

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88\% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.