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WiAi-ID: Wi-Fi-Based Domain Adaptation for Appearance-Independent Passive Person Identification | IEEE Journals & Magazine | IEEE Xplore

WiAi-ID: Wi-Fi-Based Domain Adaptation for Appearance-Independent Passive Person Identification


Abstract:

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are nonconta...Show More

Abstract:

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are noncontact, passive, and privacy-preserving. While the existing related methods and systems have achieved good performance for person identification, they also encounter many significant challenges in practical applications. Due to the propagation properties of Wi-Fi signals, the signal at the receiver will change significantly when the user’s appearance changes. This makes single-appearance trained models unusable for cross-appearance recognition tasks. To address this challenge, we propose a deep learning-based framework for appearance-independent identification using Wi-Fi signals (WiAi-ID), the core of which lies in the fact that the domain discriminator and feature extractor are trained together in an adversarial manner, thus forcing the model to extract identity-inherent features independent of human appearance, and introduces a multiscale CNN adaptation module to capture time-span-based features. We collected Wi-Fi signal data of pedestrians with different appearances. The experimental results show that WiAi-ID can effectively eliminate the impact on identification due to pedestrian appearance variations and accordingly outperforms the current state-of-the-art video and wireless signal-based recognition methods.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 1, 01 January 2024)
Page(s): 1012 - 1027
Date of Publication: 22 June 2023

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