  Existing gait recognition frameworks retrieve an identity in the gallery
based on the distance between a probe sample and the identities in the gallery.
However, existing methods often neglect that the gallery may not contain
identities corresponding to the probes, leading to recognition errors rather
than raising an alarm. In this paper, we introduce a novel uncertainty-aware
gait recognition method that models the uncertainty of identification based on
learned evidence. Specifically, we treat our recognition model as an evidence
collector to gather evidence from input samples and parameterize a Dirichlet
distribution over the evidence. The Dirichlet distribution essentially
represents the density of the probability assigned to the input samples. We
utilize the distribution to evaluate the resultant uncertainty of each probe
sample and then determine whether a probe has a counterpart in the gallery or
not. To the best of our knowledge, our method is the first attempt to tackle
gait recognition with uncertainty modelling. Moreover, our uncertain modeling
significantly improves the robustness against out-of-distribution (OOD)
queries. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on datasets with OOD queries, and can also
generalize well to other identity-retrieval tasks. Importantly, our method
outperforms the state-of-the-art by a large margin of 51.26% when the OOD query
rate is around 50% on OUMVLP.
