Transformer-Based Person Identification via Wi-Fi CSI Amplitude and Phase Perturbations

TL;DR

This paper introduces a transformer-based Wi-Fi CSI method for identifying individuals without requiring motion, achieving high accuracy and demonstrating the potential for passive, device-free person recognition using commodity hardware.

Contribution

It presents a novel transformer architecture that leverages CSI amplitude and phase for stationary person identification, along with a new dataset and preprocessing pipeline.

Findings

Achieved 99.82% classification accuracy.

Outperformed convolutional and MLP baselines.

Confirmed CSI perturbations encode biometric traits.

Abstract

Wi-Fi sensing is gaining momentum as a non-intrusive and privacy-preserving alternative to vision-based systems for human identification. However, person identification through wireless signals, particularly without user motion, remains largely unexplored. Most prior wireless-based approaches rely on movement patterns, such as walking gait, to extract biometric cues. In contrast, we propose a transformer-based method that identifies individuals from Channel State Information (CSI) recorded while the subject remains stationary. CSI captures fine-grained amplitude and phase distortions induced by the unique interaction between the human body and the radio signal. To support evaluation, we introduce a dataset acquired with ESP32 devices in a controlled indoor environment, featuring six participants observed across multiple orientations. A tailored preprocessing pipeline, including outlier removal, smoothing, and phase calibration, enhances signal quality. Our dual-branch transformer architecture processes amplitude and phase modalities separately and achieves 99.82\% classification accuracy, outperforming convolutional and multilayer perceptron baselines. These results demonstrate the discriminative potential of CSI perturbations, highlighting their capacity to encode biometric traits in a consistent manner. They further confirm the viability of passive, device-free person identification using low-cost commodity Wi-Fi hardware in real-world settings.