  Gait, i.e., the movement pattern of human limbs during locomotion, is a
promising biometric for the identification of persons. Despite significant
improvement in gait recognition with deep learning, existing studies still
neglect a more practical but challenging scenario -- unsupervised cross-domain
gait recognition which aims to learn a model on a labeled dataset then adapts
it to an unlabeled dataset. Due to the domain shift and class gap, directly
applying a model trained on one source dataset to other target datasets usually
obtains very poor results. Therefore, this paper proposes a Transferable
Neighborhood Discovery (TraND) framework to bridge the domain gap for
unsupervised cross-domain gait recognition. To learn effective prior knowledge
for gait representation, we first adopt a backbone network pre-trained on the
labeled source data in a supervised manner. Then we design an end-to-end
trainable approach to automatically discover the confident neighborhoods of
unlabeled samples in the latent space. During training, the class consistency
indicator is adopted to select confident neighborhoods of samples based on
their entropy measurements. Moreover, we explore a high-entropy-first neighbor
selection strategy, which can effectively transfer prior knowledge to the
target domain. Our method achieves state-of-the-art results on two public
datasets, i.e., CASIA-B and OU-LP.
