Real-Time Face Mask Detection
A CNN model to detect face masks in live video, achieving 98% accuracy.
Overview
This project involves building and training a Convolutional Neural Network (CNN) to accurately identify whether a person in a live video feed is wearing a face mask. The goal was to create a fast and reliable system that could be deployed in public spaces to help enforce safety regulations.
Challenges & Solutions
One of the main challenges was handling false positives (e.g., a hand near the face being detected as a mask). This was addressed by refining the dataset and implementing a more complex CNN architecture with additional convolutional layers to better capture facial features.
Key Features
- Real-time processing and analysis
- Scalable architecture design
- User-friendly interface
- Comprehensive documentation