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

Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptation