Person Re-Identification Through Wi-Fi Extracted Radio Biometric Signatures

• Published in: IEEE transactions on information forensics and security 2022, Vol.17, p.1145-1158
• Main Authors: Avola, Danilo, Cascio, Marco, Cinque, Luigi, Fagioli, Alessio, Petrioli, Chiara
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biometrics; Biometrics (access control); channel state information (CSI); Clutter; Feature extraction; Location awareness; Person re-identification; Pipeline design; radio biometric signature; Radio signals; Sensors; Signatures; Task analysis; Wi-Fi signal; Wireless communication; Wireless communications; Wireless fidelity
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2022.3158058

Person re-identification (Re-ID) is a challenging task that tries to recognize a person across different cameras, and that can prove useful in video surveillance as well as in forensics and security applications. However, traditional Re-ID systems analyzing image or video sequences suffer from well-known issues such as illumination changes, occlusions, background clutter, and long-term re-identification. To simultaneously address all these difficult problems, we explore a Re-ID solution based on an alternative medium that is inherently not affected by them, i.e., the Wi-Fi technology. The latter, due to the widespread use of wireless communications, has grown rapidly and is already enabling the development of Wi-Fi sensing applications, such as human localization or counting. These sensing procedures generally exploit Wi-Fi signals variations that are a direct consequence, among other things, of human presence, and which can be observed through the channel state information (CSI) of Wi-Fi access points. Following this rationale, in this paper, for the first time in literature, we show how the pervasive Wi-Fi technology can also be directly exploited for person Re-ID. More accurately, Wi-Fi signals amplitude and phase are extracted from CSI measurements and analyzed through a two-branch deep neural network working in a siamese-like fashion. The designed pipeline can extract meaningful features from signals, i.e., radio biometric signatures, that ultimately allow the person Re-ID. The effectiveness of the proposed system is evaluated on a specifically collected dataset, where remarkable performances are obtained; suggesting that Wi-Fi signal variations differ between different people and can consequently be used for their re-identification.