  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.
