Gait Recognition is a computer vision task aiming to identify people by their
walking patterns. Existing methods show impressive results on individual
datasets but 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. UDA for Gait
Recognition is still in its infancy and existing works proposed solutions to
limited scenarios. In this paper, we reveal a fundamental phenomenon in
adaptation of gait recognition models, in which the target domain is biased to
pose-based features rather than identity features, causing a significant
performance drop in the identification task. We suggest Gait Orientation-based
method for Unsupervised Domain Adaptation (GOUDA) to reduce this bias. To this
end, we present a novel Triplet Selection algorithm with a curriculum learning
framework, aiming to adapt the embedding space by pushing away samples of
similar poses and bringing closer samples of different poses. 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,
showing the superiority of our proposed method over prior works