Course Hive
Search

Welcome

Sign in or create your account

Continue with Google
or
Human Authentication using Gait Recognition
Play lesson

IEEE Machine Learning Projects 2025 2026 - Human Authentication using Gait Recognition

Master Cutting-Edge Machine Learning and Blockchain Innovations: Transform Ideas into Impactful Projects!

5.0 (0)
7 learners

What you'll learn

Learn techniques for detecting diseases using machine learning and image processing.
Understand fraud detection systems with machine learning applications in finance and cybersecurity.
Explore blockchain applications for secure data management and voting systems.
Develop skills in creating intelligent traffic and disaster management systems using machine learning.

This course includes

  • 33 hours of video
  • Certificate of completion
  • Access on mobile and TV

Summary

Keywords

Full Transcript

Human Authentication using Gait Recognition | IEEE Machine Learning 2022 Projects To get This Project - https://bit.ly/3KqZpmT ABSTRACT Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and viewing angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state-of-the-art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios More Projects - https://bit.ly/495LVbb Contact us on - +91 9363932473 More Projects - http://www.ieeexpert.com/ieee-python-... Ieee Xpert, India. The Best Bulk Service Provider for IEEE Solutions Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support gait recognition deep learning, gait recognition deep learning github, gait recognition project, gait recognition advantages and disadvantages, gait recognition python, biometric gait recognition, gait recognition technology, gait recognition research paper, gait recognition using cnn, gait analysis using image processing, gait recognition python code, gait recognition Kaggle, human authentication using gait recognition, human authentication using machine learning, human authentication using deep learning, human authentication using walking style, human walking style

Course Hive

Continue this lesson in the app

Install CourseHive on Android or iOS to keep learning while you move.

Related Courses

FAQs

Course Hive
Download CourseHive
Keep learning anywhere