Face Mask Detection
“The best way to predict the future is to create it.” - Peter Drucker
The Face Mask Detection project is a machine learning application designed to determine if individuals are wearing face masks. Leveraging OpenCV and Python, this model is a response to the increasing need for health safety measures.
Key Features
- Machine Learning Model: Utilizes OpenCV to accurately detect face masks on individuals.
- Real-time Detection: Capable of processing video feeds for instant feedback on mask usage.
- User Interface: Built with Node.js, HTML, and CSS to create an interactive experience.
Team Collaboration
This project was developed during the Sinhgad Hackathon, where I had the opportunity to lead a talented team of four members:
- Shreya Supekar
- Chaitanya Kulkarni
- Ishwari Dole
- Aarti Maske
Together, we combined our skills and expertise to create an effective solution for face mask detection through teamwork.
Technologies Used
- Python: For implementing the machine learning model and using OpenCV for image processing.
- Node.js: To manage the backend and handle requests.
- HTML/CSS: For creating the front-end interface for user interaction.
GitHub Repository
Explore the code and implementation details on GitHub: Face Mask Detection Repository.
Conclusion
This project illustrates the intersection of machine learning and computer vision, showcasing how technology can be employed to enhance public safety. Join me in exploring the capabilities of AI in everyday applications!
Quick Notes
- Developed a model to promote health safety.
- Real-time detection capabilities enhance functionality.
- Future improvements may include integrating additional features for enhanced accuracy.