Abstract
As inertial sensors are low-cost, easy-to-use, and can be integrated in wearable devices, they can be used to establish as a new modality for user authentication in the smart environment in which computing systems can recognize persons implicitly by their walking patterns. This motivates our proposal to use multi-region size Convolutional Neural Network to recognize users from their gait patterns recorded from accelerometers and gyroscopes in mobile and wearable devices.
Experiments on Inertial Sensor Dataset of OU-ISIR Gait Database, the largest inertial sensor-based gait database, demonstrate that our best CNN models provide the accuracy of \(96.84\%\) and EER of \(10.43\%\), better than those of existing methods. Furthermore, we also prove by experiments that by using only a subset of subjects in OU-ISIR dataset to train CNN models, our method can achieve the accuracy and EER approximately \((95.53 \pm 0.82)\%\) and \((11.60 \pm 0.98)\%\), respectively.
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This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number B2015-18-01.
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Nguyen, KT., Vo-Tran, TL., Dinh, DT., Tran, MT. (2017). Gait Recognition with Multi-region Size Convolutional Neural Network for Authentication with Wearable Sensors. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2017. Lecture Notes in Computer Science(), vol 10646. Springer, Cham. https://doi.org/10.1007/978-3-319-70004-5_14
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