Title: Face Mask Detection Using Python

Summary:

Face Mask Detection using Python is a computer vision project that automatically detects whether a person is wearing a face mask or not by analyzing images or live video feed. It uses machine learning and deep learning techniques along with OpenCV and TensorFlow or Keras libraries.

Explanation:

This project was especially useful during the COVID-19 pandemic to help enforce mask-wearing in public spaces. The system uses Python to process images or real-time video and identify faces. Once a face is detected, it classifies the face into two categories: with mask or without mask.

Here is how the project works step by step:

Dataset Preparation:
A dataset is created or downloaded that contains images of people with and without face masks. These images are labeled accordingly.

Model Training:
A deep learning model (usually a Convolutional Neural Network or CNN) is trained on the dataset. The model learns to extract features from the faces and classify them.

Face Detection:
Before checking for a mask, the face must be located in the image. OpenCV or a pre-trained face detector like Haar Cascade or SSD (Single Shot Detector) is used to detect faces in the frame.

Mask Detection:
The detected face is passed through the trained model, which predicts whether the face is wearing a mask or not.

Real-Time Implementation:
The system can be connected to a webcam. It continuously processes video frames, detects faces, checks for masks, and displays the result with a label and possibly a bounding box.

Output:
The program displays each face with a label such as “Mask” or “No Mask” in real-time. It may also use color-coded boxes – green for “Mask” and red for “No Mask”.

Technologies and Libraries Used:

Python

OpenCV

TensorFlow or Keras

NumPy

Matplotlib (for visualization, if needed)

Applications:

Security monitoring in public places

Entry gates of offices, malls, or airports

Health and safety systems