Gait recognition aims to recognize persons’ identities by walking styles. Gait recognition has unique advantages due to its characteristics of non-contact and long-distance compared with face and fingerprint recognition. Cross-view gait recognition is a challenge task because view variance may produce large impact on gait silhouettes. The development of deep learning has promoted cross-view gait recognition performances to a higher level. However, performances of existing deep learning-based cross-view gait recognition methods are limited by lack of gait samples under different views. In this paper, we take a Multi-view Gait Generative Adversarial Network (MvGGAN) to generate fake gait samples to extend existing gait datasets, which provides adequate gait samples for deep learning-based cross-view gait recognition methods. The proposed MvGGAN method trains a single generator for all view pairs involved in single or multiple datasets. Moreover, we perform domain alignment based on projected maximum mean discrepancy to reduce the influence of distribution divergence caused by sample generation. The experimental results on CASIA-B and OUMVLP dataset demonstrate that fake gait samples generated by the proposed MvGGAN method can improve performances of existing state-of-the-art cross-view gait recognition methods obviously on both single-dataset and cross-dataset evaluation settings.

中文翻译：

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多视角步态图像生成，用于跨视角步态识别

步态识别旨在通过步行方式识别人的身份。与面部和指纹识别相比，步态识别具有非接触和长距离的特点，因此具有独特的优势。跨视图步态识别是一项艰巨的任务，因为视图差异可能会对步态轮廓产生很大影响。深度学习的发展将跨步态的步态识别性能提升到了更高的水平。但是，现有的基于深度学习的交叉视图步态识别方法的性能受到不同视图下步态样本缺乏的限制。在本文中，我们采用了多视图步态生成对抗网络（MvGGAN）来生成假步态样本以扩展现有的步态数据集，这为基于深度学习的交叉视图步态识别方法提供了足够的步态样本。提出的MvGGAN方法为单个或多个数据集中涉及的所有视图对训练单个生成器。此外，我们基于预测的最大均值差异执行域对齐，以减少由样本生成引起的分布差异的影响。在CASIA-B和OUMVLP数据集上的实验结果表明，所提出的MvGGAN方法生成的假步态样本可以在单数据集和跨数据集评估上明显改善现有的最新横断面步态识别方法的性能。设置。