# TRGR: Transmissive RIS-aided Gait Recognition Through Walls

arXiv: https://arxiv.org/abs/2407.21566 · HTML: https://mlsanity.com/paper/2407.21566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Autoregressive Moving Average Graph Filtering](https://mlsanity.com/paper/1602.04436/) (2016)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning](https://mlsanity.com/paper/2208.14326/) (2022)

## Abstract (arXiv)

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.