Gait recognition is an important recognition technology, because gait is not easy to camouflage and does not need cooperation to recognize subjects. However, many existing methods are inadequate in preserving both temporal information and fine-grained information, thus reducing its discrimination. This problem is more serious when the subjects with similar walking postures are identified. In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features. Thus our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Global Distance Alignment (GDA). SFE uses Temporal Feature Fusion (TFF) and Fine-grained Feature Extraction (FFE) to effectively extract the spatio-temporal features from raw silhouettes. GDA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted spatio-temporal features. GDA can make the extracted features have low inter-class similarity and high intra-class similarity, thus enhancing their discrimination. Extensive experiments on mini-OUMVLP and CASIA-B have proved that we have a better result than some state-of-the-art methods.

翻译：Gait 承认是一种重要的识别技术,因为动作不容易伪装,不需要合作来辨认主题,然而,许多现有方法都不足以保存时间信息和精细资料,从而减少其歧视。当发现行走姿势相似的主体时,这一问题就更为严重。在本文中,我们试图从两个方面强化对时空运动特征的歧视:有效提取时空运动特征和合理改进抽取特征。因此,我们提出了方法,包括Spatio-时空采掘(SFE)和全球距离调整(GDA)。SFE使用时空变色和精细地采掘(FF)和精细细微地采掘(FFE)来有效从生的沙发上提取时空特征。GDA在现实生活中使用大量无标签的体格数据作为改进抽取的时空特征的基准。GDA可以使所提取的特征具有低级间相似性和高水平的内空调调调(GDA),从而强化了我们所展示的内空级实验和高空级实验结果。