Abstract
This study investigates biometric gait identification using accelerometer and gyroscope signals. The analysis is based on experiments conducted on the BUT gait database comprising 100 subjects and a publicly available Signet dataset with recordings from 29 individuals. The selection of data sets was based on the fact that the same group of participants was recorded on two separate data collection days, which enabled cross-day validation. We focused on creating a two-step method for generating artificial gait samples. For the pipeline involving data augmentation, we decided to add a generative component. The incorporation of the generative model component resulted in a substantial improvement in performance. For the BUT dataset, the F1-score increased from 0.802 (augmentation only) to 0.891, whereas for the Signet dataset it improved from 0.774 to 0.839.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.196
Integration of Augmentation and Generative Models for Enhanced Gait-Based Authentication
This study investigates biometric gait identification using accelerometer and gyroscope signals. The analysis is based on experiments conducted on the BUT gait database comprising 100 subjects and a publicly available Signet dataset with recordings from 29 individuals. The selection of data sets was based on the fact that the same group of participants was recorded on two separate data collection days, which enabled cross-day validation. We focused on creating a two-step method for generating artificial gait samples. For the pipeline involving data augmentation, we decided to add a generative component. The incorporation of the generative model component resulted in a substantial improvement in performance. For the BUT dataset, the F1-score increased from 0.802 (augmentation only) to 0.891, whereas for the Signet dataset it improved from 0.774 to 0.839.
Recommended Citation
Sawicki, A., Saeed, K. & Lukšys, D.(2026). Integration of Augmentation and Generative Models for Enhanced Gait-Based Authentication. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.196