Spatio-temporal Gait Feature with Global Distance Alignment

TL;DR

This paper introduces a novel gait recognition method combining spatio-temporal feature extraction with global distance alignment to improve discrimination, especially among similar walking postures, using unlabeled real-world data.

Contribution

It proposes a combined approach of Spatio-temporal Feature Extraction and Global Distance Alignment to enhance gait recognition accuracy and robustness.

Findings

Outperforms state-of-the-art methods on mini-OUMVLP and CASIA-B datasets.

Effective extraction of detailed spatio-temporal gait features.

Refinement with unlabeled data improves intra-class similarity and inter-class separation.

Abstract

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.