Abstract
Human gait seamless continuous authentication, based on wearable accelerometers, is a novel biometric instrument which can be exploited to identify the user of mobile and wearable devices. In this paper, we present a study on recognition of user identity, by analysis of gait data, collected through body inertial sensors from 175 different users. The mechanism used for identity recognition is based on deep learning machinery, specifically on a convolutional network, trained with readings from different sensors, and on filtering and buffering mechanism to increase the accuracy. Results show a very high accuracy in both recognizing known and unknown identities.
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Acknowledgements
This work has been partially funded by EU Funded projects H2020 C3ISP, GA #700294, H2020 NeCS, GA #675320 and EIT Digital HII on Trusted Cloud Management.
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Giorgi, G., Martinelli, F., Saracino, A., Sheikhalishahi, M. (2017). Try Walking in My Shoes, if You Can: Accurate Gait Recognition Through Deep Learning. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security . SAFECOMP 2017. Lecture Notes in Computer Science(), vol 10489. Springer, Cham. https://doi.org/10.1007/978-3-319-66284-8_32
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DOI: https://doi.org/10.1007/978-3-319-66284-8_32
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