TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition

• URL: http://arxiv.org/abs/2102.04621v1
• Date: Tue, 9 Feb 2021 03:07:07 GMT
• Title: TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
• Authors: Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu, Xiaoping Zhang, Tao Mei
• Abstract summary: This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
• Score: 77.77786072373942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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