Flow Chart of FaceMask Detection System

In order to train a custom face mask detector, we need to break our project into two distinct phases, each with its own respective sub-steps (as shown by Figure 1 above):

• Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk
• Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask.

We will use these images to build a CNN model using TensorFlow to detect if you are wearing a face mask by using the webcam of your PC. Additionally, you can also use your phone’s camera to do the same!

In this article, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented.

We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to implement two more additional Python scripts used to:

• Detect COVID-19 face masks in images
• Detect face masks in real-time video streams