Publication details
Interpretable Gait Recognition by Granger Causality
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | Proceedings of 26th International Conference on Pattern Recognition, ICPR 2022 |
| MU Faculty or unit | |
| Citation | |
| Web | |
| Doi | http://dx.doi.org/10.1109/ICPR56361.2022.9956624 |
| Keywords | Granger causality; gait recognition |
| Description | Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from a lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. The 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 the 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. |