Abstract
The increasing usage of smartwatches to access sensitive and personal data while being applied in health monitoring and quick payment, has given rise to the need of convenient and secure authentication technique. However, traditional memory-based authentication methods like PIN are proved to be easily cracked or user-unfriendly. This paper presents a novel approach to unlock smartwatches or authenticate users’ identities on smartwatches by analyzing a users’ handwaving patterns. A filed study was conducted to design typical smartwatch unlocking scenarios and gather users’ handwaving data. Behavioral features were extracted to accurately characterize users’ handwaving patterns. Then a one-class classification algorithm based on scaled Manhattan distance was developed to perform the task of user authentication. Extensive experiments based on a newly established 150-person-time handwaving dataset with a smartwatch, are included to demonstrate the effectiveness of the proposed approach, which achieves an equal-error rate of 4.27% in free-shaking scenario and 14.46% in imitation-attack scenario. This level of accuracy shows that these is indeed identity information in handwaving behavior that can be used as a wearable authentication mechanism.
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Aknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61403301 and Grant 61773310, in part by the China Postdoctoral Science Foundation under Grant 2014M560783 and Grant 2015T81032, in part by the Natural Science Foundation of Shaanxi Province under Grant 2015JQ6216, and in part by the Fundamental Research Funds for the Central Universities under Grant xjj2015115.
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Wang, Z., Shen, C., Chen, Y. (2017). Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_59
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DOI: https://doi.org/10.1007/978-3-319-69923-3_59
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