  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.
