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• W4285201167 abstract "By virtue of the stability of signatures and the high difficulty of imitation, handwriting signature, as an important behavioral biometric trait, has been broadly adopted for authorization and identity verification. Nowadays, the emergence of consumer-level wrist-worn devices incorporating rich sensors has profoundly changed the way of human-machine interactions, enabling new observation ways of signature. In this paper, we investigate the feasibility of authenticating users by sensing hand motions of signing in the air using fingers. Each signature is represented by the readings of the gyroscope and accelerometer compensated by device attitude readings. A recurrent neural network-based algorithm is proposed to characterize signatures and accurately determine whether a signature is from the claimed genuine user or an imposter. We empirically investigate 22 participants by recording their in-air signing gestures using smartwatch motion sensors. The verification shows that, despite the inevitable variability of repeating genuine signature drawing, forged signatures tend to show more dissimilarity than the variability. The high-precision experimental result (i.e., equal error rate of 0.83%) against insider adversaries not only demonstrates the effectiveness of our proposed approach but also indicates the feasibility of a more user-friendly signature authentication way by signing their names in the air. Moreover, we investigate the impact of properties of motion sensory data on signature authentication. In addition, we include more details of the experiments, validation of the proposed pre-processing method, and analysis of the circumvention as one of the desirable proprieties of biometrics, of signing motions by measuring the skill of forgery." @default.
• W4285201167 created "2022-07-14" @default.
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• W4285201167 date "2022-01-01" @default.
• W4285201167 modified "2023-10-14" @default.
• W4285201167 title "Sensing In-Air Signature Motions Using Smartwatch: A High-Precision Approach of Behavioral Authentication" @default.
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• W4285201167 doi "https://doi.org/10.1109/access.2022.3177905" @default.
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