Single Person Identification Using Wi-Fi Signals

Over time, identification devices and applications have evolved. However, they still require people to perform certain actions to obtain their identification. Recently, Channel State Information (CSI) has emerged as a promising technology that resonates strongly in the evolution of identification systems. CSI allows obtaining information on the state of the transmission channel in Wi-Fi networks and offers a high granularity of data that can help the identification of a single person. In this work, we propose a new methodology for identifying a person using Wi-Fi CSI data. For this, we pre-process the received signal to eliminate unwanted propagation effects, such as noise and outliers, and also to smooth the signal. Then we use the signal amplitude of different Wi-Fi subcarriers as features for machine learning models to perform a person identification. Our proposal was validated with a CSI dataset collected from different participants performing diverse activities. The obtained results confirm the applicability of the proposed methodology as we obtained 92% average precision for identifying different people.