Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.

Personalized Gait-based AuthenticationUsing UWB Wearable Devices

Alessio Vecchio
2019-01-01

Abstract

Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.
2019
978-1-4503-6021-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/988721
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