Wi-Fi Passive Person Re-Identification based on Channel State Information

TL;DR

This paper explores Wi-Fi-based person re-identification using Channel State Information and neural networks, demonstrating promising results with a newly acquired dataset due to the instability of traditional RSSI-based methods.

Contribution

It introduces a novel approach using SNR from CSI measurements combined with neural networks for person Re-ID, and provides a new dataset for this task.

Findings

SNR from CSI can effectively distinguish individuals for Re-ID

Neural networks improve accuracy in Wi-Fi-based person identification

Preliminary results show promising potential of the proposed method

Abstract

With the increasing need for wireless data transfer, Wi-Fi networks have rapidly grown in recent years providing high throughput and easy deployment. Nowadays, Access Points (APs) can be found easily wherever we go, therefore Wi-Fi sensing applications have caught a great deal of interest from the research community. Since human presence and movement influence the Wi-Fi signals transmitted by APs, it is possible to exploit those signals for person Re-Identification (Re-ID) task. Traditional techniques for Wi-Fi sensing applications are usually based on the Received Signal Strength Indicator (RSSI) measurement. However, recently, due to the RSSI instability, the researchers in this field propose Channel State Information (CSI) measurement based methods. In this paper we explain how changes in Signal Noise Ratio (SNR), obtained from CSI measurements, combined with Neural Networks can be used for person Re-ID achieving remarkable preliminary results. Due to the lack of available public data in the current state-of-the-art to test such type of task, we acquired a dataset that properly fits the aforementioned task.