  As a unique biometric that can be perceived at a distance, gait has broad
applications in person authentication, social security, and so on. Existing
gait recognition methods suffer from changes in viewpoint and clothing and
barely consider extracting diverse motion features, a fundamental
characteristic in gaits, from gait sequences. This paper proposes a novel
motion modeling method to extract the discriminative and robust representation.
Specifically, we first extract the motion features from the encoded motion
sequences in the shallow layer. Then we continuously enhance the motion feature
in deep layers. This motion modeling approach is independent of mainstream work
in building network architectures. As a result, one can apply this motion
modeling method to any backbone to improve gait recognition performance. In
this paper, we combine motion modeling with one commonly used backbone~(GaitGL)
as GaitGL-M to illustrate motion modeling. Extensive experimental results on
two commonly-used cross-view gait datasets demonstrate the superior performance
of GaitGL-M over existing state-of-the-art methods.
