Identity recognition is increasingly used to control access to sensitive data, restricted areas in industrial, healthcare, and defense settings, as well as in consumer electronics. To this end, existing approaches are typically based on collecting and analyzing biometric data and imply severe privacy con-cerns. Particularly when cameras are involved, users might even reject or dismiss an identity recognition system. Furthermore, iris or fingerprint scanners, cameras, microphones, etc., imply installation and maintenance costs and require the user's active participation in the recognition procedure. This paper proposes a non-intrusive identity recognition system based on analyzing WiFi's Channel State Information (CSI). We show that CSI data attenuated by a person's body and typical movements allows for a reliable identification - even in a sitting posture. We further propose a lightweight deep learning algorithm trained using CSI data, which we implemented and evaluated on an embedded platform (i.e., a Raspberry Pi 4B). Our results obtained using real-world experiments suggest a high accuracy in recognizing people's identity, with a specificity of 98% and a sensitivity of 99%, while requiring a low training effort and negligible cost.
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