@inproceedings{f798930fd8c74f89ba60019bbd58b836,
title = "Single Person Identification Using Wi-Fi Signals",
abstract = "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.",
keywords = "Amplitude, CSI, Identification, Person, Wi-Fi",
author = "Soto, \{Julio C.H.\} and Iandra Galdino and Egberto Caballero and Debora Muchaluat-Saade and Celio Albuquerque",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Latin-American Conference on Communications, LATINCOM 2023 ; Conference date: 15-11-2023 Through 17-11-2023",
year = "2023",
doi = "10.1109/LATINCOM59467.2023.10361872",
language = "Ingl{\'e}s",
series = "Proceedings - 2023 IEEE Latin-American Conference on Communications, LATINCOM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Ramiro Velazquez",
booktitle = "Proceedings - 2023 IEEE Latin-American Conference on Communications, LATINCOM 2023",
}