Recurrent neural network for inertial gait user recognition in smartphones

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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.
This article belongs to the Special Issue Sensors for Gait Biometrics
Biometrics, Gait Recognition, Pattern Recognition, Recurrent Neural Network, Smartphone
Bibliographic citation
Fernandez-Lopez P... [et al.]. Recurrent Neural Network for Inertial Gait User Recognition in Smartphones. Sensors. 2019; 19(18):4054.