Researchers have demonstrated that Wi-Fi signals can be used to uniquely identify individuals based on their physical characteristics and movements. One study from the Aeronautics Institute of Technology in Brazil explored how Wi-Fi Channel State Information (CSI) can capture the unique shape and structure of a person's palm, enabling touchless access control by analyzing how a hand distorts the signal. Using a Raspberry Pi and machine learning algorithms, the system was able to distinguish between individuals by the way their hands affected the Wi-Fi signals, suggesting a new method for biometric authentication without physical contact.
Separately, German researchers at the Karlsruhe Institute of Technology investigated the use of Wi-Fi beamforming technology to identify individuals by their gait. By analyzing unencrypted beamforming feedback signals from Wi-Fi routers, they found that it is possible to infer a person's identity based on how their walking style alters the radio signals. This research highlights the potential for widespread surveillance, as any beamforming-enabled router could theoretically be used to track individuals without their knowledge, raising significant privacy and security concerns.

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Researchers disclosed a method that uses Wi-Fi beamforming signals to identify individuals by physical traits such as gait and palm-related characteristics for touchless access control. The references describe this as a research development rather than a breach, patch, or law enforcement action.
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