# Silhouette based View embeddings for Gait Recognition under Multiple Views

arXiv: https://arxiv.org/abs/2108.05524 · HTML: https://mlsanity.com/paper/2108.05524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Multi-Task Multi-Sensor Fusion for 3D Object Detection](https://mlsanity.com/paper/2012.12397/) (2020)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition](https://mlsanity.com/paper/1702.04710/) (2017)

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Spatial Transformer Network on Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.03873/) (2022)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [Gait Recognition Using 3-D Human Body Shape Inference](https://mlsanity.com/paper/2212.09042/) (2022)
- [GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework](https://mlsanity.com/paper/2203.03966/) (2022)
- [Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2308.06707/) (2023)
- [Free Lunch for Gait Recognition: A Novel Relation Descriptor](https://mlsanity.com/paper/2308.11487/) (2023)
- [Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition](https://mlsanity.com/paper/2501.16608/) (2025)

## Abstract (arXiv)

Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information of view angle is ignored to some extent. Instead of direct view estimation and training view-specific recognition models, we propose a compatible framework that can embed view information into existing architectures of gait recognition. The embedding is simply achieved by a selective projection layer. Experimental results on two large public datasets show that the proposed framework is very effective.