Hand Gesture Recognition System
A real-time system that interprets hand gestures and converts them into actionable commands using computer vision techniques.
Project Overview
This hand gesture recognition system was developed to create a natural interface between humans and computers. The system can recognize a variety of hand gestures in real-time and translate them into commands that can control applications, smart home devices, or assistive technologies.
Technical Implementation
The system implements a complete pipeline for gesture recognition:
- Image Acquisition: Captures video feed from standard webcam
- Pre-processing: Applies filters and transformations to enhance hand features
- Hand Detection: Uses contour analysis and convex hull techniques
- Feature Extraction: Identifies key points and geometric features
- Classification: Machine learning model matches features to known gestures
- Action Mapping: Translates recognized gestures to system commands
Key Features
Gesture Library
Recognizes 15+ standard gestures with customizable mappings
Real-time
Processes 30fps with <100ms latency on standard hardware
Adaptive
Adjusts to different lighting conditions and skin tones
Integrations
Pre-built connectors for common OS and smart home platforms
Performance Metrics
| Metric | Value |
|---|---|
| Accuracy | 94.2% (controlled environment) |
| Recognition Speed | 28ms per frame |
| Minimum Hardware | 2GHz CPU, no GPU required |
| Supported OS | Windows, Linux, macOS |
Applications
- Accessibility: Assistive technology for mobility-impaired users
- Smart Homes: Contactless control of IoT devices
- Presentations: Navigate slides without physical devices
- Gaming: Alternative input method for immersive experiences
- Education: Interactive learning tools