Interpretable Gait Recognition by Granger Causality

0567047 - ÚI 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Balazia, M. - Hlaváčková-Schindler, Kateřina - Sojka, P. - Plant, C.
Interpretable Gait Recognition by Granger Causality.
2022 26th International Conference on Pattern Recognition (ICPR). Piscataway: IEEE, 2022, s. 1069-1075. ISBN 978-1-6654-9063-4. ISSN 1051-4651.
[ICPR 2022: International Conference on Pattern Recognition /26./. Montréal (CA), 21.08.2022-25.08.2022]
Grant CEP: GA ČR(CZ) GA19-16066S
Institucionální podpora: RVO:67985807
Klíčová slova: Measurement * Analytical models * Three-dimensional displays * Neural networks * Video surveillance * Skeleton * Motion capture
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.1109/ICPR56361.2022.9956624

Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the standardized motion capture format, constituting a set of 3D joint spatial trajectories, is envisaged as a causal system of joints interacting in time. We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait. We evaluate eleven distance functions in the GGM feature space by established classification and class-separability evaluation metrics. Our experiments indicate that, depending on the metric, the most appropriate distance functions for the GGM are the total norm distance and the Ky-Fan 1-norm distance. Experiments also show that the GGM is able to detect the most discriminative joint interactions and that it outperforms five related interpretable models in correct classification rate and in Davies-Bouldin index. The proposed GGM model can serve as a complementary tool for gait analysis in kinesiology or for gait recognition in video surveillance.
Trvalý link: https://hdl.handle.net/11104/0338380