  Gait Recognition is a computer vision task aiming to identify people by their
walking patterns. Although existing methods often show high performance on
specific datasets, they lack the ability to generalize to unseen scenarios.
Unsupervised Domain Adaptation (UDA) tries to adapt a model, pre-trained in a
supervised manner on a source domain, to an unlabelled target domain. There are
only a few works on UDA for gait recognition proposing solutions to limited
scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of
gait recognition models, caused by the bias in the target domain to viewing
angle or walking direction. We then suggest a remedy to reduce this bias with a
novel triplet selection strategy combined with curriculum learning. To this
end, we present Gait Orientation-based method for Unsupervised Domain
Adaptation (GOUDA). We provide extensive experiments on four widely-used gait
datasets, CASIA-B, OU-MVLP, GREW, and Gait3D, and on three backbones, GaitSet,
GaitPart, and GaitGL, justifying the view bias and showing the superiority of
our proposed method over prior UDA works.
