Application of Generative Models to Augment IMU Signals in Gait Biometrics
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-37720-4_22
Reference11 articles.
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2. Zou, Q., Wang, Y., Wang, Q., et al.: Deep training-based gait recognition using smartphones in the wild. arXiv:1811.00338 (2020)
3. Delgado-Escano, R., Castro, F.M., Cozar, J.R., et al.: An end-to-end multi-task and fusion CNN for inertial-based gait recognition. IEEE Access 7 (2019)
4. Huang, H., Zhou, P., Li, Y., et al.: A Lightweight attention-based CNN model for efficient gait recognition with wearable IMU sensors. Sensors 21, 2866 (2021). https://doi.org/10.3390/s21082866(2021)
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1. Generation Of Synthetic Data for Behavioral Gait Biometrics;International Conference on Information Systems Development;2024-09-09
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