Face Mask Detection using scikit-learn* (sklearn): Developer Spotlight

Masks and face coverings are one of the most effective tools for protecting the health of individuals against respiratory diseases. During COVID-19, face masks played a vital role as we have to slow or stop the spread of virus. Face Mask Detection is an advanced technology that can help in identifying whether a person is wearing a mask or not.

Ripan Roy in his blog proposed a solution to Face Mask Detection using Support Vector Machine (SVM) and Intel® Extension for Scikit-learn*.

The blog explains the various steps involved in the project:

1. Installation of required libraries
2. Data Preprocessing
3. Model Training
4. Model Testing

Read and learn more about the project at Face-Mask Detection using SVM — Intel oneAPI Optimized Scikit-Learn Library.

Learn more about Intel Extension for Scikit-learn!!!

Intel Extension for Scikit-learn: This Intel Extension seamlessly speeds up your scikit-learn applications for Intel® architectures across single and multi-node configurations. Also, this extension package dynamically patches scikit-learn estimators while improving performance for your machine learning algorithms. By using Scikit-learn with this extension, the performance for training and inference can be improved up to 100x with the equivalent mathematical accuracy.

Download Intel Extension for Scikit-learn as part of the Intel® AI Analytics Toolkit (AI Kit).

What’s Next?

We encourage you to learn more about and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow. Also, visit AI & ML page covering Intel’s AI software development resources for preparing, building, deploying, and scaling your AI solutions.

For more details about the new 4th Gen Intel® Xeon® Scalable processors, visit Intel's AI Solution Platform portal where you can learn how Intel is empowering developers to run end-to-end AI pipelines on these powerful CPUs.

About the Author:

Ripan Roy is currently pursuing his bachelor’s in computer science and business systems at Techno India University in West Bengal, India. His major interests are software engineering, machine learning and deep learning.