Data Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning Approaches
Gait Recognition · Machine Learning · Deep Learning
Academic research reference · 2023
Data Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning Approaches
Pattern Recognition Letters · Elsevier
Gait Recognition · Machine Learning · Deep Learning
Publication record
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier
- Published
- 2023
- Research area
- Gait Recognition · Machine Learning · Deep Learning
Research summary
A comparative study of data preprocessing and feature selection in gait recognition using machine learning and deep learning approaches.
Research topics
Research keywords
Research reference
Research → capability → product
This publication informs a research foundation. Connections below describe GaitAI's documented architecture; they do not establish product accuracy, clinical validation or regulatory clearance.
Gait recognition & biometrics
See this paper in GaitScape →Modules directly informed at capability level
Post-event video investigation
Product signals: Trajectory & path · Gait identity · Behaviour patterns
Policy-governed watchlist matching for authorized deployments
Product signals: Gait identity
Shared architecture · 11 additional modules
These modules share platform capabilities. The publication does not directly address their applications.
Related publications
All publications
Advancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition Techniques
Comparative Study of Machine Learning and Deep Learning Techniques for Gait Recognition: Advances in Feature Extraction, Reduction, Transformation, and Classification
