  In the field of gait recognition from motion capture data, designing
human-interpretable gait features is a common practice of many fellow
researchers. To refrain from ad-hoc schemes and to find maximally
discriminative features we may need to explore beyond the limits of human
interpretability. This paper contributes to the state-of-the-art with a machine
learning approach for extracting robust gait features directly from raw joint
coordinates. The features are learned by a modification of Linear Discriminant
Analysis with Maximum Margin Criterion so that the identities are maximally
separated and, in combination with an appropriate classifier, used for gait
recognition. Experiments on the CMU MoCap database show that this method
outperforms eight other relevant methods in terms of the distribution of
biometric templates in respective feature spaces expressed in four class
separability coefficients. Additional experiments indicate that this method is
a leading concept for rank-based classifier systems.
