This paper conducts a feasibility study regarding the use of the Wi-Fi channel state information for user recognition based on in-air handwritten signatures. A novel system for identity recognition is thus proposed to observe for distinctive signal distortions along the propagation path for different users. The system capitalizes on the vast availability of Wi-Fi signals for signal analysis without needing additional hardware infra-structure. Since the patterns of the raw Wi-Fi signals are sensitive to the signer's location, a transfer learning has been adopted to cope with the positional variation. Specifically, features trained at one position are transferred to classify signals collected at another position via a single shot retraining. A kernel and range space projection has been adopted for the single shot retraining. Our experiments show encouraging results for the proposed system.

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