ArticleSensors (Basel, Switzerland)2022
Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 23 citations in OpenAlex.
- Development and Performance Analysis of a Semi-Supervised Gait Recognition Model for Pediatric Abnormalities Using a Hybrid Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Securing gait recognition with homomorphic encryption.Scientific reports · 2025Article
- GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition.PeerJ. Computer science · 2025Article
- Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.Sensors (Basel, Switzerland) · 2023Article
- A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigation, surveillance, and access control. Existing approaches for gait recognition generally belong to the supervised learning domain, where all samples in the dataset are annotated. In the real world, annotation is often expensive and time-consuming. Moreover, convolutional neural networks (CNNs) have dominated the field of gait recognition for many years and have been extensively researched, while other recent methods such as vision transformer (ViT) remain unexplored. In this manuscript, we propose a self-supervised learning (SSL) approach for pretraining the feature extractor using the DINO model to automatically learn useful gait features with the vision transformer architecture. The feature extractor is then used for extracting gait features on which the fully connected neural network classifier is trained using the supervised approach. Experiments on CASIA-B and OU-MVLP gait datasets show the effectiveness of the proposed approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.