  Gait is a unique biometric feature that can be recognized at a distance;
thus, it has broad applications in crime prevention, forensic identification,
and social security. To portray a gait, existing gait recognition methods
utilize either a gait template which makes it difficult to preserve temporal
information, or a gait sequence that maintains unnecessary sequential
constraints and thus loses the flexibility of gait recognition. In this paper,
we present a novel perspective that utilizes gait as a deep set, which means
that a set of gait frames are integrated by a global-local fused deep network
inspired by the way our left- and right-hemisphere processes information to
learn information that can be used in identification. Based on this deep set
perspective, our method is immune to frame permutations, and can naturally
integrate frames from different videos that have been acquired under different
scenarios, such as diverse viewing angles, different clothes, or different
item-carrying conditions. Experiments show that under normal walking
conditions, our single-model method achieves an average rank-1 accuracy of
96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait
dataset. Under various complex scenarios, our model also exhibits a high level
of robustness. It achieves accuracies of 90.8% and 70.3% on CASIA-B under
bag-carrying and coat-wearing walking conditions respectively, significantly
outperforming the best existing methods. Moreover, the proposed method
maintains a satisfactory accuracy even when only small numbers of frames are
available in the test samples; for example, it achieves 85.0% on CASIA-B even
when using only 7 frames. The source code has been released at
https://github.com/AbnerHqC/GaitSet.
