Gesture Recognition Final Year Projects with Source Code

Byadmin Final Year Projects

Gesture Recognition Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Gesture Recognition projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Gesture Recognition Final Year Projects

  1. A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition
    This project develops a lightweight system to recognize hand gestures. The system, called LHGR-Net, works on small devices like a Raspberry Pi. It can detect gestures in real time and use them to control home appliances. The method uses less memory but still performs almost as well as advanced models.
  2. Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture
    This project focuses on building a system that can automatically recognize sign language gestures. It uses cameras instead of costly sensors to capture signs. The system learns both the hand movements and facial expressions to understand gestures accurately. Advanced neural networks process these features to identify signs from Indian and Russian sign languages more reliably than existing methods.
  3. SIGNFORMER DeepVision Transformer for Sign Language Recognition
    This project focuses on helping people communicate with the hearing impaired by recognizing sign language automatically. The researchers used a vision transformer model to identify static Indian sign language gestures. Their method breaks hand gestures into small parts and analyzes them using a transformer network. The system is accurate, fast to train, and performs better than previous approaches.
  4. Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis
    This project focuses on using muscle signals from the arm to control prosthetic hands for people with physical disabilities. The researchers collected signals from volunteers performing different hand movements and processed the data to remove noise and reduce complexity. They then used machine learning and neural networks to classify these movements accurately. The system achieved high accuracy, showing it can reliably recognize and control hand motions in real time.
  5. Dynamic Gesture Recognition Based on Three-Stream Coordinate Attention Network and Knowledge Distillation
    This project focuses on recognizing hand gestures from videos more accurately and quickly. It uses a new method called 3SCKI that helps the system focus on gestures and ignore background distractions. The model learns efficiently from existing data and can even recognize gestures it has not seen before. Tests show it performs very well on a large gesture dataset for different types of video data.
  6. Smart Healthcare Hand Gesture Recognition Using CNN-Based Detector and Deep Belief Network
    This project develops a system that can accurately track and recognize hand gestures from videos in real-world environments. It processes video frames, cleans the images, and uses neural networks to identify hand movements. The system then extracts detailed features, optimizes them to reduce errors, and classifies gestures using a deep learning model. Tests on standard datasets show it achieves high accuracy and works well compared to existing methods.
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How We Help You with Gesture Recognition Projects

At Final Year Projects, we provide complete guidance for Gesture Recognition IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

Our team has over 10 years of experience guiding students in Computer Science, Electronics, Electrical, and other engineering domains. We support students across India, including Hyderabad, Mumbai, Bangalore, Chennai, Pune, Delhi, Ahmedabad, Kolkata, Jaipur and Surat. International students in the USA, Canada, UK, Singapore, Australia, Malaysia, and Thailand also benefit from our expert guidance.

Gesture Recognition Project Synopsis & Presentation

Final Year Projects helps prepare Gesture Recognition project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Gesture Recognition Project Thesis Writing

Final Year Projects provides thesis writing services for Gesture Recognition projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

All theses are checked with plagiarism check tools to guarantee originality and quality. Fast-track services are available for urgent submissions. Hundreds of students have successfully completed their projects and theses with our support.

Gesture Recognition Research Paper Support

We offer complete support for Gesture Recognition research papers. Services include writing, editing, and proofreading for journals and conferences.

We accept Word, RTF, and LaTeX formats. Every paper is reviewed to meet IEEE and publication standards, improving acceptance chances. Our guidance ensures that students produce high-quality, publication-ready research papers.

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