Face Recognition System
Project Overview
This project focuses on creating a smart home system that connects household devices to an IoT network, enabling users to control lights, fans, doors, and other appliances remotely. The system integrates smart sensors for motion detection, temperature monitoring, and fire alerts, providing real-time updates and customizable automation routines. The smart home system is compatible with voice commands and mobile apps, making it highly user-friendly and secure.
Key Features
Face Detection
Remotely switch on/off lights, fans, and appliances via a mobile app.
Identity Recognition
Track energy usage and appliance status in real time.
Feature Extraction
Detect motion, temperature, and humidity for automated responses.
Multi-Face Support
Receive instant notifications for unauthorized access or fire hazards.
Image processing
Compatible with smart assistants like Alexa or Google Assistant (optional).
Performance Optimization
Set timers and schedules for appliances to turn on/off automatically.
Technologies used
- Programming Language: Python
- Libraries: OpenCV, TensorFlow, Dlib
- Frameworks: Keras, FaceNet
- Hardware Integration: Camera modules (USB/webcam)
- Cloud Services: AWS Rekognition or Google Vision API
Why Choose This Project?
- ✅ Ideal for mastering computer vision tools and deep learning frameworks.
- ✅ Hands-on experience with libraries like OpenCV and TensorFlow.
- ✅ Build proficiency in training and deploying facial recognition systems.
Deliverables From Our Side
- → Complete source code and project documentation
- → Pre-trained model weights and training scripts
- → Demo video showcasing face recognition in action
