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.

本文提出了一种名为TraND的跨域步态识别框架，通过深度学习和无监督学习，使用经过训练的模型来自动地发现未标记样本的自信邻域，然后使用高熵优先邻域选择策略来转移先前学习到的信息，从而对CASIA-B和OU-LP等两个公共数据集进行跨域步态识别，并取得了最新的最佳结果。

TraND: 无监督跨领域步态识别的可转移邻域发现