HandPass: A Wi-Fi CSI Palm Authentication Approach for Access Control

TL;DR

This paper introduces HandPass, a Wi-Fi CSI-based palm authentication system that achieves high accuracy in user identification by analyzing biometric features reflected in Wi-Fi signals.

Contribution

It presents a novel palm recognition approach using Wi-Fi CSI data, demonstrating high classification accuracy with a Raspberry Pi setup and multiple algorithms.

Findings

Random Forest achieved 99.82% F1-Score

High accuracy with amplitude and phase data

Effective biometric features extracted from Wi-Fi CSI

Abstract

Wi-Fi Channel State Information (CSI) has been extensively studied for sensing activities. However, its practical application in user authentication still needs to be explored. This study presents a novel approach to biometric authentication using Wi-Fi Channel State Information (CSI) data for palm recognition. The research delves into utilizing a Raspberry Pi encased in a custom-built box with antenna power reduced to 1dBm, which was used to capture CSI data from the right hands of 20 participants (10 men and 10 women). The dataset was normalized using MinMax scaling to ensure uniformity and accuracy. By focusing on biophysical aspects such as hand size, shape, angular spread between fingers, and finger phalanx lengths, among other characteristics, the study explores how these features affect electromagnetic signals, which are then reflected in Wi-Fi CSI, allowing for precise user identification. Five classification algorithms were evaluated, with the Random Forest classifier achieving an average F1-Score of 99.82\% using 10-fold cross-validation. Amplitude and Phase data were used, with each capture session recording approximately 1000 packets per second in five 5-second intervals for each User. This high accuracy highlights the potential of Wi-Fi CSI in developing robust and reliable user authentication systems based on palm biometric data.