# Spatio-temporal Gait Feature with Global Distance Alignment

arXiv: https://arxiv.org/abs/2203.03376 · HTML: https://mlsanity.com/paper/2203.03376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping](https://mlsanity.com/paper/2005.08625/) (2020)
- [Multimodal feature fusion for CNN-based gait recognition: an empirical comparison](https://mlsanity.com/paper/1806.07753/) (2018)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1606.03238/) (2016)
- [Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules](https://mlsanity.com/paper/2010.09084/) (2020)
- [Robust Gait Recognition by Integrating Inertial and RGBD Sensors](https://mlsanity.com/paper/1610.09816/) (2016)
- [Towards Human Body-Part Learning for Model-Free Gait Recognition](https://mlsanity.com/paper/1904.01620/) (2019)
- [Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN](https://mlsanity.com/paper/2009.12516/) (2020)

## Abstract (arXiv)

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.