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

2025-07-17

AI Summary

This work addresses non-contact human identification under stationary conditions without motion dependency. We propose a novel Transformer-based method leveraging dual-modal Wi-Fi channel state information (CSI), jointly modeling amplitude and phase perturbations. To our knowledge, this is the first application of the Transformer architecture to stationary Wi-Fi biometrics. Our approach employs a two-branch network to separately capture discriminative features from CSI amplitude and phase, accompanied by customized preprocessing—including outlier removal, sliding-average smoothing, and phase calibration—and a newly constructed benchmark dataset. Experiments conducted on low-power ESP32 hardware achieve 99.82% subject identification accuracy on the proprietary dataset, substantially outperforming CNN and MLP baselines. This study demonstrates the feasibility of using off-the-shelf Wi-Fi devices for seamless, contactless authentication and establishes a new lightweight paradigm for wireless biometric recognition.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Identifies stationary individuals using Wi-Fi CSI perturbations
• Extracts biometric traits from amplitude and phase distortions
• Enables device-free person identification with low-cost hardware

Innovation

• Transformer-based identification using stationary CSI
• Dual-branch architecture processing amplitude and phase
• Low-cost Wi-Fi hardware for passive identification