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By virtue of the stability of signatures and the high difficulty of imitation, handwriting signatures, as an important behavioral biometric trait, have been broadly adopted for authorization and identity verification. The emergence of consumer-level wrist-worn devices incorporating rich sensors has profoundly changed human-machine interactions, enabling new signature observation method. In this study, we investigate the feasibility of authenticating users by sensing hand motions of signing in air using fingers. Each signature is represented by the readings of the gyroscope and accelerometer which are compensated by the 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 variability. The high-precision experimental result (i.e., equal error rate of 0.83&#x0025;) against insider adversaries not only demonstrates the effectiveness of our proposed approach but also indicates the feasibility of a more user-friendly signature authentication method by signing their names in the air. Moreover, we investigate the impact of the 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 properties of biometrics of signing motions by measuring the skill of forgery.

Sensing In-Air Signature Motions Using Smartwatch: A High-Precision Approach of Behavioral Authentication