  Gait recognition, referring to the identification of individuals based on the
manner in which they walk, can be very challenging due to the variations in the
viewpoint of the camera and the appearance of individuals. Current methods for
gait recognition have been dominated by deep learning models, notably those
based on partial feature representations. In this context, we propose a novel
deep network, learning to transfer multi-scale partial gait representations
using capsules to obtain more discriminative gait features. Our network first
obtains multi-scale partial representations using a state-of-the-art deep
partial feature extractor. It then recurrently learns the correlations and
co-occurrences of the patterns among the partial features in forward and
backward directions using Bi-directional Gated Recurrent Units (BGRU). Finally,
a capsule network is adopted to learn deeper part-whole relationships and
assigns more weights to the more relevant features while ignoring the spurious
dimensions. That way, we obtain final features that are more robust to both
viewing and appearance changes. The performance of our method has been
extensively tested on two gait recognition datasets, CASIA-B and OU-MVLP, using
four challenging test protocols. The results of our method have been compared
to the state-of-the-art gait recognition solutions, showing the superiority of
our model, notably when facing challenging viewing and carrying conditions.
