  Deep learning research has made many biometric recognition solution viable,
but it requires vast training data to achieve real-world generalization. Unlike
other biometric traits, such as face and ear, gait samples cannot be easily
crawled from the web to form massive unconstrained datasets. As the human body
has been extensively studied for different digital applications, one can rely
on prior shape knowledge to overcome data scarcity. This work follows the
recent trend of fitting a 3D deformable body model into gait videos using deep
neural networks to obtain disentangled shape and pose representations for each
frame. To enforce temporal consistency in the network, we introduce a new
Linear Dynamical Systems (LDS) module and loss based on Koopman operator
theory, which provides an unsupervised motion regularization for the periodic
nature of gait, as well as a predictive capacity for extending gait sequences.
We compare LDS to the traditional adversarial training approach and use the USF
HumanID and CASIA-B datasets to show that LDS can obtain better accuracy with
less training data. Finally, we also show that our 3D modeling approach is much
better than other 3D gait approaches in overcoming viewpoint variation under
normal, bag-carrying and clothing change conditions.
