Wi-Fi based user identification using in-air handwritten signature

• Main Authors: Jung, Junsik, Moon, Han-Cheol, Kim, Jooyoung, Kim, Donghyun, Toh, Kar-Ann
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Biometrics; Wi-Fi
• Online Access: https://hdl.handle.net/10356/153537
• Institution: Nanyang Technological University

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.