- Whofi uses Wi-Fi signal distortions to fingerprint individuals without visual data
- Deep neurons network cards are signal changes to identify people with almost perfect precision
- University research opens up new debates on confidentiality on biometric monitoring via Wi-Fi signals
Researchers at the University Sapienza in Rome have created Whofi, a system that claims to be able to identify individuals by analyzing Wi-Fi signals.
The system follows people by interpreting how their presence disrupts Wi-Fi models, offering a potential alternative to conventional biometric methods.
The technology works by examining information on the condition of the channels, or CSI, which measures changes in Wi -Fi signals caused by people and objects – and a deep neural network then interprets these disturbances as individual fingerprints.
The researchers say that the system offers 95.5% precision in the identification of people, even under different environmental conditions.
The team behind Whofi includes Danilo Avola, Daniele Pannone, Dario Montagnini and Emad Emam, who previously proposed a system called Eyefi in 2020. The new system is more precise and capable of re-identifying people via non-visual biometric signatures integrated into CSI.
Whofi does not rely on cameras or physical contact. It only needs an existing Wi-Fi network to feel human presence and movement.
Technology can work in darkness, through walls and even around obstacles, making it a discreet option compared to video surveillance systems.
The researchers underline that the Whofi does not collect personal data or does not reveal identities in the conventional sense, noting: “By taking advantage of the non-visual biometric characteristics integrated in the Wi-Fi CSI, this study offers a confidentiality approach and a robust approach for Wi-Fi rehabilitists.
However, it is clear that the ability to follow individuals without their knowledge is a potential confidentiality nightmare.
Routine intimacy violations can reveal models of daily behavior, such as locations or regular movements, potentially exposing sensitive personal habits.
Until now, Whofi remains an academic project without plans known for commercial or government deployment. However, the advantages of surveillance capacity are clear. It can bypass low lighting and crowded environments and is less visible than visual cameras or scanners.
A number of similar Wi-Fi detection technologies have surfaced in various forms over the years.
Gamgee has developed an autumn detection system that could alert others if someone fell or if an intruder entered the house.
Comcast Xfinity Service has introduced Wi-Fi movement, which transforms daily devices such as intelligent refrigerators, printers or motion sensors.
Other researchers have gone further, using Wi-Fi signals to “see” through the walls. A UC Santa Barbara team has created a system that describes objects and even reads letters through barriers.
A similar study from Carnegie Mellon University has demonstrated how standard Wi-Fi routers can detect the location and position of the body of a person in a room.
You can find out more about the search behind Whofi in this article published on the arxiv pre -printed server.
Via Tech Xplore
