Abstract
With the increasing popularity of wearable devices, it is common to use several smart devices simultaneously including smartphones. With embedded accelerometers and gyroscopes, the smart devices naturally constitute a multiple sensor system to measure the activities of the user more comprehensively and accurately. This paper proposed a new approach to perform authentication by using motion data collected from both wearables and smartphones. We propose a set of simple timedomain features to characterize the motion data collected from daily activities such as walking and train a one-class classifier to differentiate legitimate and illegitimate users. The experiments on data collected from 20 subjects demonstrate the proposed multiple sensor approach does lead to obvious performance improvements compared with traditional single sensor approaches.
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Acknowledgments
The research is supported by NFSC (61175039, 61221063, 61403301), 863 High Tech Development Plan (2012AA011003), Research Fund for Doctoral Program of Higher Education of China (20090201120032), International Research Collaboration Project of Shaanxi Province (2013KW11) and Fundamental Research Funds for Central Universities (2012jdhz08).
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Dong, J., Cai, Z. (2016). User Authentication Using Motion Sensor Data from Both Wearables and Smartphones. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_83
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DOI: https://doi.org/10.1007/978-3-319-46654-5_83
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