WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding

TL;DR

WhoFi introduces a Wi-Fi-based person re-identification system using CSI signals and deep learning, providing an alternative to visual methods that face challenges like occlusion and poor lighting.

Contribution

The paper presents a novel Wi-Fi signal processing pipeline with a Transformer-based neural network for person re-identification, demonstrating competitive accuracy.

Findings

Achieves competitive re-identification accuracy on NTU-Fi dataset.

Utilizes CSI signals and deep neural networks for biometric feature extraction.

Shows robustness against visual data limitations like occlusion.

Abstract

Person Re-Identification is a key and challenging task in video surveillance. While traditional methods rely on visual data, issues like poor lighting, occlusion, and suboptimal angles often hinder performance. To address these challenges, we introduce WhoFi, a novel pipeline that utilizes Wi-Fi signals for person re-identification. Biometric features are extracted from Channel State Information (CSI) and processed through a modular Deep Neural Network (DNN) featuring a Transformer-based encoder. The network is trained using an in-batch negative loss function to learn robust and generalizable biometric signatures. Experiments on the NTU-Fi dataset show that our approach achieves competitive results compared to state-of-the-art methods, confirming its effectiveness in identifying individuals via Wi-Fi signals.