In-air Signature Authentication Using Smartwatch Motion Sensors

Abstract

Handwriting signature, as an important behavioral biometric trait, has been broadly adopted for authorization and identity verification. Nowadays, the emergence of consumer-level devices and the development of deep neural networks have vastly facilitated this field. In this paper, we present a practical recurrent neural network-based in-air signature authentication system using smartwatch. The signature is represented by the readings of gyroscope and accelerometer compensated by device attitude readings. The system can extract features from in-air signing motions to verify whether a signature is from an imposter or the genuine user. Moreover, an in-air signature dataset, consisting of 3190 signature pairs from 22 participants, is built for validation. Experimental results demonstrate that our proposed approach achieves an equal error rate(EER) of 0.83%. Besides, we investigate the impact of properties of motion sensory data on in-air signature authentication.