  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.
