Overview / Usage

The project aims to enhance safety in public spaces by leveraging AI to detect face masks using the Intel OneAPI Scikit-Learn Library. This technology addresses the pressing need for enforcing mask-wearing protocols, especially in crowded areas, to mitigate the spread of airborne diseases like COVID-19. The research and development work involve training machine learning models to accurately identify individuals wearing masks, thereby aiding in real-time monitoring and ensuring compliance with safety measures. In production, this solution can be deployed in various settings such as airports, hospitals, schools, and public transportation, contributing significantly to public health and safety efforts.

Technologies Used

1. Scikit-Learn: Leveraged for machine learning and data preprocessing tasks.
2. Python: Programming language used for developing face mask detection algorithms.
3. OpenCV: Computer vision library utilized for image processing and analysis.
4. Jupyter Notebook or Google Colab: Interactive development environments for prototyping and experimenting with code.
5. Intel Distribution of Python: Optimized Python distribution for performance on Intel hardware.
6. Intel DevCloud: Cloud-based development and testing platform provided by Intel for optimizing and validating code on Intel architectures.

Repository

https://github.com/rppadmakumar3/FaceDetectionUsingOneAPI