RT Journal Article
T1 Recurrent neural network for inertial gait user recognition in smartphones
A1 Fernandez Lopez, Pablo
A1 Liu Jiménez, Judith
A1 Kiyokawa, Kiyoshi
A1 Wu, Yang
A1 Sanchez-Reillo, Raul
AB In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.
PB MDPI
SN 14248220 (ISSN)
YR 2019
FD 2019-09-19
LK https://hdl.handle.net/10016/30488
UL https://hdl.handle.net/10016/30488
LA eng
NO This article belongs to the Special Issue Sensors for Gait Biometrics
NO This work was partially supported by the Spanish National Cybersecurity Institute (INCIBE) under the Grants Program “Excellence of Advanced Cybersecurity Research Teams.” The work of this paper has been partly funded by PREVIEW project, granted by the Spanish Ministry of Economy and Competence, with the code TEC2015-68784-R (MINECO/FEDER). This project is partially funded by Microsoft Research Asia
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RD 1 jul. 2024