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

• URL: http://arxiv.org/abs/2507.12854v1
• Date: Thu, 17 Jul 2025 07:26:07 GMT
• Title: Transformer-Based Person Identification via Wi-Fi CSI Amplitude and Phase Perturbations
• Authors: Danilo Avola, Andrea Bernardini, Francesco Danese, Mario Lezoche, Maurizio Mancini, Daniele Pannone, Amedeo Ranaldi,
• Abstract summary: Wi-Fi sensing is gaining momentum as a non-intrusive and privacy-preserving alternative to vision-based systems for human identification.<n>Most prior wireless-based approaches rely on movement patterns, such as walking gait, to extract biometric cues.<n>We propose a transformer-based method that identifies individuals from Channel State Information recorded while the subject remains stationary.
• Score: 4.595408704451027
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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